Last updated on 2025-04-04 12:52:37 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.3.4 | 4.53 | 218.76 | 223.29 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.3.4 | 2.88 | 146.94 | 149.82 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.3.4 | 368.80 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.3.4 | 393.75 | ERROR | |||
| r-devel-macos-arm64 | 0.3.4 | 220.00 | OK | |||
| r-devel-macos-x86_64 | 0.3.4 | 354.00 | OK | |||
| r-devel-windows-x86_64 | 0.3.4 | 7.00 | 202.00 | 209.00 | OK | |
| r-patched-linux-x86_64 | 0.3.4 | 4.02 | 208.15 | 212.17 | OK | |
| r-release-linux-x86_64 | 0.3.4 | 3.67 | 203.67 | 207.34 | OK | |
| r-release-macos-arm64 | 0.3.4 | 164.00 | OK | |||
| r-release-macos-x86_64 | 0.3.4 | 257.00 | OK | |||
| r-release-windows-x86_64 | 0.3.4 | 7.00 | 201.00 | 208.00 | OK | |
| r-oldrel-macos-arm64 | 0.3.4 | OK | ||||
| r-oldrel-macos-x86_64 | 0.3.4 | 299.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.3.4 | 7.00 | 242.00 | 249.00 | OK |
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [152s/199s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250404_044251.]: Entered 'clean_dt'-Function
[20250404_044251.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044251.]: got experimental data
[20250404_044251.]: Entered 'clean_dt'-Function
[20250404_044251.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044251.]: got calibration data
[20250404_044251.]: ### Starting with regression calculations ###
[20250404_044251.]: Entered 'regression_type1'-Function
[20250404_044252.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044252.]: Logging df_agg: CpG#1
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044252.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044252.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044252.]: Entered 'cubic_regression'-Function
[20250404_044252.]: 'cubic_regression': minmax = FALSE
[20250404_044252.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044252.]: Logging df_agg: CpG#2
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044252.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044252.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044253.]: Logging df_agg: CpG#3
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044253.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044253.]: Logging df_agg: CpG#4
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044253.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044253.]: Logging df_agg: CpG#5
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044253.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044252.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044252.]: Logging df_agg: CpG#6
[20250404_044252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044252.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044252.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044252.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044253.]: Entered 'cubic_regression'-Function
[20250404_044253.]: 'cubic_regression': minmax = FALSE
[20250404_044253.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044253.]: Logging df_agg: CpG#7
[20250404_044253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044253.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044253.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044253.]: Entered 'hyperbolic_regression'-Function
[20250404_044253.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044254.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044254.]: Logging df_agg: CpG#8
[20250404_044254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044254.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044254.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044254.]: Entered 'hyperbolic_regression'-Function
[20250404_044254.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044254.]: Entered 'cubic_regression'-Function
[20250404_044254.]: 'cubic_regression': minmax = FALSE
[20250404_044254.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044254.]: Logging df_agg: CpG#9
[20250404_044254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044254.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044254.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044254.]: Entered 'hyperbolic_regression'-Function
[20250404_044254.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044255.]: Entered 'cubic_regression'-Function
[20250404_044255.]: 'cubic_regression': minmax = FALSE
[20250404_044255.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044255.]: Logging df_agg: row_means
[20250404_044255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044255.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044255.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044255.]: Entered 'hyperbolic_regression'-Function
[20250404_044255.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044255.]: Entered 'cubic_regression'-Function
[20250404_044255.]: 'cubic_regression': minmax = FALSE
[20250404_044259.]: Entered 'regression_type1'-Function
[20250404_044300.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044300.]: Logging df_agg: CpG#1
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044300.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044300.]: Entered 'cubic_regression'-Function
[20250404_044300.]: 'cubic_regression': minmax = FALSE
[20250404_044300.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044300.]: Logging df_agg: CpG#2
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044300.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044301.]: Logging df_agg: CpG#3
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044301.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044301.]: Logging df_agg: CpG#4
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044301.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044302.]: Logging df_agg: CpG#5
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044302.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044300.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044300.]: Logging df_agg: CpG#6
[20250404_044300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044300.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044300.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044300.]: Entered 'hyperbolic_regression'-Function
[20250404_044300.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044301.]: Logging df_agg: CpG#7
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044301.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044301.]: Entered 'cubic_regression'-Function
[20250404_044301.]: 'cubic_regression': minmax = FALSE
[20250404_044301.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044301.]: Logging df_agg: CpG#8
[20250404_044301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044301.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044301.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044301.]: Entered 'hyperbolic_regression'-Function
[20250404_044301.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044302.]: Logging df_agg: CpG#9
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044302.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044302.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044302.]: Logging df_agg: row_means
[20250404_044302.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044302.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044302.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044302.]: Entered 'hyperbolic_regression'-Function
[20250404_044302.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044302.]: Entered 'cubic_regression'-Function
[20250404_044302.]: 'cubic_regression': minmax = FALSE
[20250404_044305.]: Entered 'clean_dt'-Function
[20250404_044305.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044305.]: got experimental data
[20250404_044305.]: Entered 'clean_dt'-Function
[20250404_044305.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044305.]: got calibration data
[20250404_044305.]: ### Starting with regression calculations ###
[20250404_044305.]: Entered 'regression_type1'-Function
[20250404_044305.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044305.]: Logging df_agg: CpG#1
[20250404_044305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044305.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044305.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044305.]: Entered 'hyperbolic_regression'-Function
[20250404_044305.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044306.]: Entered 'cubic_regression'-Function
[20250404_044306.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044306.]: Logging df_agg: CpG#2
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044306.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044306.]: Entered 'cubic_regression'-Function
[20250404_044306.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044306.]: Logging df_agg: CpG#3
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044306.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044307.]: Logging df_agg: CpG#4
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044307.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044307.]: Logging df_agg: CpG#5
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044307.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044306.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044306.]: Logging df_agg: CpG#6
[20250404_044306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044306.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044306.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044306.]: Entered 'hyperbolic_regression'-Function
[20250404_044306.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044307.]: Logging df_agg: CpG#7
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044307.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044307.]: Entered 'cubic_regression'-Function
[20250404_044307.]: 'cubic_regression': minmax = FALSE
[20250404_044307.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044307.]: Logging df_agg: CpG#8
[20250404_044307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044307.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044307.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044307.]: Entered 'hyperbolic_regression'-Function
[20250404_044307.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044308.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044308.]: Logging df_agg: CpG#9
[20250404_044308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044308.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044308.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044308.]: Entered 'hyperbolic_regression'-Function
[20250404_044308.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044308.]: Entered 'cubic_regression'-Function
[20250404_044308.]: 'cubic_regression': minmax = FALSE
[20250404_044308.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044308.]: Logging df_agg: row_means
[20250404_044308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044308.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044308.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044308.]: Entered 'hyperbolic_regression'-Function
[20250404_044308.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044309.]: Entered 'cubic_regression'-Function
[20250404_044309.]: 'cubic_regression': minmax = FALSE
[20250404_044311.]: Entered 'regression_type1'-Function
[20250404_044312.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044312.]: Logging df_agg: CpG#1
[20250404_044312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044312.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044312.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044312.]: Entered 'hyperbolic_regression'-Function
[20250404_044312.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044313.]: Logging df_agg: CpG#2
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044313.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044313.]: Logging df_agg: CpG#3
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044313.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044314.]: Logging df_agg: CpG#4
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044314.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044314.]: Logging df_agg: CpG#5
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044314.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044312.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044313.]: Logging df_agg: CpG#6
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044313.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044313.]: Logging df_agg: CpG#7
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044313.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044313.]: Entered 'hyperbolic_regression'-Function
[20250404_044313.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044313.]: Entered 'cubic_regression'-Function
[20250404_044313.]: 'cubic_regression': minmax = FALSE
[20250404_044313.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044313.]: Logging df_agg: CpG#8
[20250404_044313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044313.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044313.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044314.]: Logging df_agg: CpG#9
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044314.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044314.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044314.]: Logging df_agg: row_means
[20250404_044314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044314.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044314.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044314.]: Entered 'hyperbolic_regression'-Function
[20250404_044314.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044314.]: Entered 'cubic_regression'-Function
[20250404_044314.]: 'cubic_regression': minmax = FALSE
[20250404_044316.]: Entered 'solving_equations'-Function
[20250404_044316.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_044316.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_044316.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_044316.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_044316.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_044316.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_044316.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_044316.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250404_044316.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250404_044316.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250404_044316.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250404_044316.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250404_044316.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250404_044316.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250404_044316.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250404_044316.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_044316.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_044316.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_044316.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_044316.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_044316.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_044316.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_044316.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_044316.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_044316.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_044316.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_044316.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_044316.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_044316.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_044316.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_044316.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250404_044316.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250404_044316.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250404_044316.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250404_044316.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250404_044316.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250404_044316.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250404_044316.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250404_044316.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_044316.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_044316.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_044316.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_044316.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_044316.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_044316.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_044316.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_044316.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_044316.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_044316.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_044316.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_044316.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_044316.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_044316.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_044316.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_044316.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250404_044316.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250404_044316.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250404_044316.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250404_044316.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250404_044316.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250404_044316.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250404_044316.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250404_044316.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250404_044316.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250404_044316.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250404_044317.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250404_044317.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250404_044317.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250404_044317.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250404_044317.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250404_044317.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250404_044317.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250404_044317.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250404_044317.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250404_044317.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_044317.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_044317.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_044317.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_044317.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_044317.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_044317.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_044317.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_044317.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_044317.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_044317.]: ### Starting with regression calculations ###
[20250404_044317.]: Entered 'regression_type1'-Function
[20250404_044318.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_044318.]: Logging df_agg: CpG#1
[20250404_044318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044318.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250404_044318.]: Entered 'hyperbolic_regression'-Function
[20250404_044318.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_044319.]: Logging df_agg: CpG#2
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_044319.]: Logging df_agg: CpG#3
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_044319.]: Logging df_agg: CpG#4
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_044320.]: Logging df_agg: CpG#5
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_044319.]: Logging df_agg: CpG#6
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044319.]: Entered 'cubic_regression'-Function
[20250404_044319.]: 'cubic_regression': minmax = FALSE
[20250404_044319.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_044319.]: Logging df_agg: CpG#7
[20250404_044319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044319.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250404_044319.]: Entered 'hyperbolic_regression'-Function
[20250404_044319.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_044320.]: Logging df_agg: CpG#8
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_044320.]: Logging df_agg: CpG#9
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044320.]: Entered 'cubic_regression'-Function
[20250404_044320.]: 'cubic_regression': minmax = FALSE
[20250404_044320.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_044320.]: Logging df_agg: row_means
[20250404_044320.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044320.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250404_044320.]: Entered 'hyperbolic_regression'-Function
[20250404_044320.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044321.]: Entered 'cubic_regression'-Function
[20250404_044321.]: 'cubic_regression': minmax = FALSE
[20250404_044322.]: Entered 'solving_equations'-Function
[20250404_044322.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250404_044322.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250404_044322.]: ### Starting with regression calculations ###
[20250404_044322.]: Entered 'regression_type1'-Function
[20250404_044323.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_044323.]: Logging df_agg: CpG#1
[20250404_044323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044323.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250404_044323.]: Entered 'hyperbolic_regression'-Function
[20250404_044323.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044324.]: Entered 'cubic_regression'-Function
[20250404_044324.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_044324.]: Logging df_agg: CpG#2
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044324.]: Entered 'cubic_regression'-Function
[20250404_044324.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_044324.]: Logging df_agg: CpG#3
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_044325.]: Logging df_agg: CpG#4
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_044325.]: Logging df_agg: CpG#5
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044324.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_044324.]: Logging df_agg: CpG#6
[20250404_044324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044324.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250404_044324.]: Entered 'hyperbolic_regression'-Function
[20250404_044324.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_044325.]: Logging df_agg: CpG#7
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_044325.]: Logging df_agg: CpG#8
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_044325.]: Logging df_agg: CpG#9
[20250404_044325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044325.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250404_044325.]: Entered 'hyperbolic_regression'-Function
[20250404_044325.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044325.]: Entered 'cubic_regression'-Function
[20250404_044325.]: 'cubic_regression': minmax = FALSE
[20250404_044325.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_044326.]: Logging df_agg: row_means
[20250404_044326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044326.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250404_044326.]: Entered 'hyperbolic_regression'-Function
[20250404_044326.]: 'hyperbolic_regression': minmax = FALSE
[20250404_044326.]: Entered 'cubic_regression'-Function
[20250404_044326.]: 'cubic_regression': minmax = FALSE
[20250404_044327.]: Entered 'solving_equations'-Function
[20250404_044327.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250404_044327.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250404_044327.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250404_044327.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250404_044327.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250404_044327.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250404_044327.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250404_044327.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250404_044327.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250404_044327.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250404_044327.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250404_044327.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250404_044327.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250404_044327.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250404_044327.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250404_044327.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250404_044327.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250404_044327.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250404_044327.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250404_044327.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250404_044327.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250404_044327.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250404_044327.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250404_044327.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250404_044327.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250404_044327.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250404_044327.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250404_044327.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250404_044327.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044327.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250404_044327.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250404_044327.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250404_044327.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250404_044327.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250404_044327.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250404_044327.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250404_044327.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250404_044327.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250404_044327.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250404_044327.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250404_044327.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250404_044327.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250404_044327.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250404_044327.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250404_044327.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250404_044327.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250404_044327.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250404_044327.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250404_044327.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044327.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250404_044327.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250404_044327.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250404_044327.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250404_044327.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250404_044327.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250404_044327.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250404_044327.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250404_044327.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250404_044327.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250404_044327.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250404_044327.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250404_044327.]: Entered 'solving_equations'-Function
[20250404_044327.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250404_044327.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250404_044327.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250404_044327.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250404_044327.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250404_044327.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250404_044327.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250404_044327.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250404_044327.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250404_044327.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250404_044327.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250404_044327.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250404_044327.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250404_044327.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250404_044327.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250404_044327.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250404_044327.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250404_044327.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250404_044327.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250404_044327.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250404_044327.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250404_044327.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250404_044328.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250404_044328.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250404_044328.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250404_044328.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250404_044328.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250404_044328.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250404_044328.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250404_044328.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250404_044328.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250404_044328.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250404_044328.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250404_044328.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250404_044328.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250404_044328.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250404_044328.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250404_044328.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250404_044328.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250404_044328.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250404_044328.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250404_044328.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250404_044328.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250404_044328.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250404_044328.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250404_044328.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250404_044328.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250404_044328.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250404_044328.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250404_044328.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250404_044328.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250404_044328.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250404_044328.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250404_044329.]: Entered 'clean_dt'-Function
[20250404_044329.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044329.]: got experimental data
[20250404_044329.]: Entered 'clean_dt'-Function
[20250404_044329.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044329.]: got calibration data
[20250404_044329.]: ### Starting with regression calculations ###
[20250404_044329.]: Entered 'regression_type1'-Function
[20250404_044330.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044330.]: Logging df_agg: CpG#1
[20250404_044330.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044330.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044330.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044330.]: Entered 'hyperbolic_regression'-Function
[20250404_044330.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044330.]: Entered 'cubic_regression'-Function
[20250404_044330.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044330.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044330.]: Logging df_agg: CpG#2
[20250404_044330.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044330.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044330.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044330.]: Entered 'hyperbolic_regression'-Function
[20250404_044330.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: Entered 'cubic_regression'-Function
[20250404_044331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044331.]: Logging df_agg: CpG#3
[20250404_044331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044331.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044331.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044331.]: Entered 'hyperbolic_regression'-Function
[20250404_044331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044332.]: Logging df_agg: CpG#4
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044332.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044332.]: Logging df_agg: CpG#5
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044332.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: Entered 'cubic_regression'-Function
[20250404_044333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044331.]: Logging df_agg: CpG#6
[20250404_044331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044331.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044331.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044331.]: Entered 'hyperbolic_regression'-Function
[20250404_044331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044331.]: Entered 'cubic_regression'-Function
[20250404_044331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044332.]: Logging df_agg: CpG#7
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044332.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044332.]: Logging df_agg: CpG#8
[20250404_044332.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044332.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044332.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044332.]: Entered 'hyperbolic_regression'-Function
[20250404_044332.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044332.]: Entered 'cubic_regression'-Function
[20250404_044332.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044333.]: Logging df_agg: CpG#9
[20250404_044333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044333.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044333.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044333.]: Entered 'hyperbolic_regression'-Function
[20250404_044333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: Entered 'cubic_regression'-Function
[20250404_044333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044333.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044333.]: Logging df_agg: row_means
[20250404_044333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044333.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044333.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044333.]: Entered 'hyperbolic_regression'-Function
[20250404_044333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044334.]: Entered 'cubic_regression'-Function
[20250404_044334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044337.]: Entered 'regression_type1'-Function
[20250404_044338.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044338.]: Logging df_agg: CpG#1
[20250404_044338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044338.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044338.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044338.]: Entered 'hyperbolic_regression'-Function
[20250404_044338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044339.]: Logging df_agg: CpG#2
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044339.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044339.]: Logging df_agg: CpG#3
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044339.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044340.]: Logging df_agg: CpG#4
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044340.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044341.]: Logging df_agg: CpG#5
[20250404_044341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044341.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044341.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044341.]: Entered 'hyperbolic_regression'-Function
[20250404_044341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044338.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044339.]: Logging df_agg: CpG#6
[20250404_044339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044339.]: Entered 'hyperbolic_regression'-Function
[20250404_044339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044339.]: Entered 'cubic_regression'-Function
[20250404_044339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044340.]: Logging df_agg: CpG#7
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044340.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: Entered 'cubic_regression'-Function
[20250404_044340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044340.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044340.]: Logging df_agg: CpG#8
[20250404_044340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044340.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044340.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044340.]: Entered 'hyperbolic_regression'-Function
[20250404_044340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044341.]: Logging df_agg: CpG#9
[20250404_044341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044341.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044341.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044341.]: Entered 'hyperbolic_regression'-Function
[20250404_044341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044341.]: Entered 'cubic_regression'-Function
[20250404_044341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044342.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044342.]: Logging df_agg: row_means
[20250404_044342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044342.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044342.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044342.]: Entered 'hyperbolic_regression'-Function
[20250404_044342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044342.]: Entered 'cubic_regression'-Function
[20250404_044342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044345.]: Entered 'clean_dt'-Function
[20250404_044345.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044345.]: got experimental data
[20250404_044345.]: Entered 'clean_dt'-Function
[20250404_044345.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044345.]: got calibration data
[20250404_044345.]: ### Starting with regression calculations ###
[20250404_044345.]: Entered 'regression_type1'-Function
[20250404_044345.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044345.]: Logging df_agg: CpG#1
[20250404_044345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044345.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044345.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044345.]: Entered 'hyperbolic_regression'-Function
[20250404_044345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: Entered 'cubic_regression'-Function
[20250404_044346.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044346.]: Logging df_agg: CpG#2
[20250404_044346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044346.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044346.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044346.]: Entered 'hyperbolic_regression'-Function
[20250404_044346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044347.]: Logging df_agg: CpG#3
[20250404_044347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044347.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044347.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044347.]: Entered 'hyperbolic_regression'-Function
[20250404_044347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044348.]: Logging df_agg: CpG#4
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044348.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044348.]: Logging df_agg: CpG#5
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044348.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044346.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044346.]: Logging df_agg: CpG#6
[20250404_044346.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044346.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044346.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044346.]: Entered 'hyperbolic_regression'-Function
[20250404_044346.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044347.]: Logging df_agg: CpG#7
[20250404_044347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044347.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044347.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044347.]: Entered 'hyperbolic_regression'-Function
[20250404_044347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044347.]: Entered 'cubic_regression'-Function
[20250404_044347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044348.]: Logging df_agg: CpG#8
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044348.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: Entered 'cubic_regression'-Function
[20250404_044348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044348.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044348.]: Logging df_agg: CpG#9
[20250404_044348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044348.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044348.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044348.]: Entered 'hyperbolic_regression'-Function
[20250404_044348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044349.]: Logging df_agg: row_means
[20250404_044349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044349.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044349.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044349.]: Entered 'hyperbolic_regression'-Function
[20250404_044349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044349.]: Entered 'cubic_regression'-Function
[20250404_044349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044352.]: Entered 'regression_type1'-Function
[20250404_044353.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044354.]: Logging df_agg: CpG#1
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250404_044354.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: Entered 'cubic_regression'-Function
[20250404_044354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044354.]: Logging df_agg: CpG#2
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250404_044354.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044355.]: Logging df_agg: CpG#3
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250404_044355.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044356.]: Logging df_agg: CpG#4
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250404_044356.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044356.]: Logging df_agg: CpG#5
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250404_044356.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044354.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044354.]: Logging df_agg: CpG#6
[20250404_044354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044354.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250404_044354.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250404_044354.]: Entered 'hyperbolic_regression'-Function
[20250404_044354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044355.]: Logging df_agg: CpG#7
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250404_044355.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: Entered 'cubic_regression'-Function
[20250404_044355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044355.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044355.]: Logging df_agg: CpG#8
[20250404_044355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044355.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250404_044355.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250404_044355.]: Entered 'hyperbolic_regression'-Function
[20250404_044355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044356.]: Logging df_agg: CpG#9
[20250404_044356.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044356.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250404_044356.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250404_044356.]: Entered 'hyperbolic_regression'-Function
[20250404_044356.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044356.]: Entered 'cubic_regression'-Function
[20250404_044356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044357.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044357.]: Logging df_agg: row_means
[20250404_044357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044357.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250404_044357.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250404_044357.]: Entered 'hyperbolic_regression'-Function
[20250404_044357.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044357.]: Entered 'cubic_regression'-Function
[20250404_044357.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044358.]: Entered 'solving_equations'-Function
[20250404_044358.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_044358.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_044358.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_044358.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_044358.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_044358.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_044358.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_044358.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_044358.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044358.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044358.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250404_044358.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250404_044358.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250404_044358.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250404_044358.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250404_044358.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250404_044358.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250404_044359.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250404_044359.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_044359.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_044359.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_044359.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_044359.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_044359.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_044359.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_044359.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_044359.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_044359.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_044359.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_044359.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_044359.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_044359.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_044359.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250404_044359.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250404_044359.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250404_044359.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250404_044359.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250404_044359.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250404_044359.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250404_044359.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_044359.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_044359.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_044359.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_044359.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_044359.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_044359.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_044359.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_044359.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_044359.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_044359.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_044359.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_044359.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_044359.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_044359.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250404_044359.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250404_044359.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250404_044359.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250404_044359.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250404_044359.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250404_044359.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250404_044359.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_044359.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_044359.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_044359.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_044359.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_044359.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_044359.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_044359.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_044359.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_044359.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_044359.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_044359.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_044359.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_044359.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_044359.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_044359.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044359.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044359.]: ### Starting with regression calculations ###
[20250404_044359.]: Entered 'regression_type1'-Function
[20250404_044400.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_044400.]: Logging df_agg: CpG#1
[20250404_044400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044400.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250404_044400.]: Entered 'hyperbolic_regression'-Function
[20250404_044400.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: Entered 'cubic_regression'-Function
[20250404_044401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_044401.]: Logging df_agg: CpG#2
[20250404_044401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044401.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250404_044401.]: Entered 'hyperbolic_regression'-Function
[20250404_044401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: Entered 'cubic_regression'-Function
[20250404_044401.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_044402.]: Logging df_agg: CpG#3
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_044402.]: Logging df_agg: CpG#4
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_044403.]: Logging df_agg: CpG#5
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044401.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_044401.]: Logging df_agg: CpG#6
[20250404_044401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044401.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250404_044401.]: Entered 'hyperbolic_regression'-Function
[20250404_044401.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_044402.]: Logging df_agg: CpG#7
[20250404_044402.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044402.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250404_044402.]: Entered 'hyperbolic_regression'-Function
[20250404_044402.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044402.]: Entered 'cubic_regression'-Function
[20250404_044402.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_044403.]: Logging df_agg: CpG#8
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: Entered 'cubic_regression'-Function
[20250404_044403.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044403.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_044403.]: Logging df_agg: CpG#9
[20250404_044403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044403.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250404_044403.]: Entered 'hyperbolic_regression'-Function
[20250404_044403.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: Entered 'cubic_regression'-Function
[20250404_044404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_044404.]: Logging df_agg: row_means
[20250404_044404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044404.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250404_044404.]: Entered 'hyperbolic_regression'-Function
[20250404_044404.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044404.]: Entered 'cubic_regression'-Function
[20250404_044404.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044405.]: Entered 'solving_equations'-Function
[20250404_044405.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044405.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044405.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250404_044405.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044405.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044405.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044406.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044406.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250404_044406.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044406.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250404_044406.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044406.]: ### Starting with regression calculations ###
[20250404_044406.]: Entered 'regression_type1'-Function
[20250404_044407.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_044407.]: Logging df_agg: CpG#1
[20250404_044407.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044407.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250404_044407.]: Entered 'hyperbolic_regression'-Function
[20250404_044407.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044407.]: Entered 'cubic_regression'-Function
[20250404_044407.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_044408.]: Logging df_agg: CpG#2
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: Entered 'cubic_regression'-Function
[20250404_044408.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_044408.]: Logging df_agg: CpG#3
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_044409.]: Logging df_agg: CpG#4
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_044409.]: Logging df_agg: CpG#5
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044407.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_044408.]: Logging df_agg: CpG#6
[20250404_044408.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044408.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250404_044408.]: Entered 'hyperbolic_regression'-Function
[20250404_044408.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044408.]: Entered 'cubic_regression'-Function
[20250404_044408.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_044409.]: Logging df_agg: CpG#7
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: Entered 'cubic_regression'-Function
[20250404_044409.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044409.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_044409.]: Logging df_agg: CpG#8
[20250404_044409.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044409.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250404_044409.]: Entered 'hyperbolic_regression'-Function
[20250404_044409.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_044410.]: Logging df_agg: CpG#9
[20250404_044410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044410.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250404_044410.]: Entered 'hyperbolic_regression'-Function
[20250404_044410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: Entered 'cubic_regression'-Function
[20250404_044410.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044410.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_044410.]: Logging df_agg: row_means
[20250404_044410.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250404_044410.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250404_044410.]: Entered 'hyperbolic_regression'-Function
[20250404_044410.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044411.]: Entered 'cubic_regression'-Function
[20250404_044411.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250404_044412.]: Entered 'solving_equations'-Function
[20250404_044412.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250404_044412.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250404_044412.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250404_044412.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250404_044412.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250404_044412.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250404_044412.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250404_044412.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250404_044412.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250404_044412.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044412.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250404_044412.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250404_044412.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250404_044412.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250404_044412.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250404_044412.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250404_044412.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250404_044412.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250404_044412.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250404_044412.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250404_044412.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250404_044412.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250404_044412.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250404_044412.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250404_044412.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250404_044412.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250404_044412.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250404_044412.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250404_044412.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250404_044412.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250404_044412.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250404_044412.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250404_044412.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250404_044412.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250404_044412.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250404_044412.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250404_044412.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250404_044412.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250404_044412.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250404_044412.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250404_044412.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250404_044412.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250404_044412.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250404_044412.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250404_044412.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250404_044412.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250404_044412.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250404_044412.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250404_044412.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250404_044412.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044412.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250404_044412.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250404_044412.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250404_044412.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250404_044412.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250404_044412.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250404_044413.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250404_044413.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250404_044413.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250404_044413.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250404_044413.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250404_044413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250404_044413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250404_044413.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250404_044413.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250404_044413.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250404_044413.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250404_044413.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250404_044413.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250404_044413.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250404_044413.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250404_044413.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250404_044413.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250404_044413.]: Entered 'solving_equations'-Function
[20250404_044413.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250404_044413.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250404_044413.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250404_044413.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250404_044413.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250404_044413.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250404_044413.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250404_044413.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250404_044413.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250404_044413.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250404_044413.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250404_044413.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250404_044413.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250404_044413.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250404_044413.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250404_044413.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250404_044413.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250404_044413.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250404_044413.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250404_044413.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250404_044413.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250404_044413.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250404_044413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044413.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250404_044413.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250404_044413.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250404_044413.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250404_044413.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250404_044413.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250404_044413.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250404_044413.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250404_044413.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250404_044413.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250404_044413.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250404_044413.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250404_044413.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250404_044413.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250404_044413.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250404_044413.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250404_044413.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250404_044413.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250404_044413.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250404_044413.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250404_044413.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250404_044413.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250404_044413.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250404_044413.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250404_044413.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250404_044413.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250404_044413.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250404_044413.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250404_044413.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250404_044413.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250404_044413.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250404_044413.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250404_044413.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250404_044413.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250404_044517.]: Entered 'clean_dt'-Function
[20250404_044517.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044517.]: got experimental data
[20250404_044517.]: Entered 'clean_dt'-Function
[20250404_044517.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250404_044517.]: got experimental data
[20250404_044518.]: Entered 'clean_dt'-Function
[20250404_044518.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044518.]: got calibration data
[20250404_044518.]: Entered 'clean_dt'-Function
[20250404_044518.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250404_044518.]: got calibration data
[20250404_044518.]: Entered 'hyperbolic_regression'-Function
[20250404_044518.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [100s/122s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250403_172136.]: Entered 'clean_dt'-Function
[20250403_172136.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172136.]: got experimental data
[20250403_172136.]: Entered 'clean_dt'-Function
[20250403_172136.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172136.]: got calibration data
[20250403_172136.]: ### Starting with regression calculations ###
[20250403_172136.]: Entered 'regression_type1'-Function
[20250403_172136.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172136.]: Logging df_agg: CpG#1
[20250403_172136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172136.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172136.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172136.]: Entered 'hyperbolic_regression'-Function
[20250403_172136.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172137.]: Logging df_agg: CpG#2
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172137.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172137.]: Logging df_agg: CpG#3
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172137.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172137.]: Logging df_agg: CpG#4
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172137.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172137.]: Logging df_agg: CpG#5
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172137.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172137.]: Logging df_agg: CpG#6
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172137.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172137.]: Logging df_agg: CpG#7
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172137.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172137.]: Entered 'cubic_regression'-Function
[20250403_172137.]: 'cubic_regression': minmax = FALSE
[20250403_172137.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172137.]: Logging df_agg: CpG#8
[20250403_172137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172137.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172137.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172137.]: Entered 'hyperbolic_regression'-Function
[20250403_172137.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172138.]: Entered 'cubic_regression'-Function
[20250403_172138.]: 'cubic_regression': minmax = FALSE
[20250403_172138.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172138.]: Logging df_agg: CpG#9
[20250403_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172138.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172138.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172138.]: Entered 'hyperbolic_regression'-Function
[20250403_172138.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172138.]: Entered 'cubic_regression'-Function
[20250403_172138.]: 'cubic_regression': minmax = FALSE
[20250403_172138.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172138.]: Logging df_agg: row_means
[20250403_172138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172138.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172138.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172138.]: Entered 'hyperbolic_regression'-Function
[20250403_172138.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172138.]: Entered 'cubic_regression'-Function
[20250403_172138.]: 'cubic_regression': minmax = FALSE
[20250403_172140.]: Entered 'regression_type1'-Function
[20250403_172141.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172141.]: Logging df_agg: CpG#1
[20250403_172141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172141.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172141.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172141.]: Entered 'hyperbolic_regression'-Function
[20250403_172141.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172141.]: Entered 'cubic_regression'-Function
[20250403_172141.]: 'cubic_regression': minmax = FALSE
[20250403_172141.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172141.]: Logging df_agg: CpG#2
[20250403_172141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172141.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172141.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172141.]: Entered 'hyperbolic_regression'-Function
[20250403_172141.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172141.]: Entered 'cubic_regression'-Function
[20250403_172141.]: 'cubic_regression': minmax = FALSE
[20250403_172141.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172141.]: Logging df_agg: CpG#3
[20250403_172141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172141.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172141.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172141.]: Entered 'hyperbolic_regression'-Function
[20250403_172141.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172141.]: Entered 'cubic_regression'-Function
[20250403_172141.]: 'cubic_regression': minmax = FALSE
[20250403_172141.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172141.]: Logging df_agg: CpG#4
[20250403_172141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172141.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172141.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172141.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172142.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172142.]: Logging df_agg: CpG#5
[20250403_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172142.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172142.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172142.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172141.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172141.]: Logging df_agg: CpG#6
[20250403_172141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172141.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172141.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172141.]: Entered 'hyperbolic_regression'-Function
[20250403_172141.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172142.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172142.]: Logging df_agg: CpG#7
[20250403_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172142.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172142.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172142.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172142.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172142.]: Logging df_agg: CpG#8
[20250403_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172142.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172142.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172142.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172142.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172142.]: Logging df_agg: CpG#9
[20250403_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172142.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172142.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172142.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172142.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172142.]: Logging df_agg: row_means
[20250403_172142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172142.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172142.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172142.]: Entered 'hyperbolic_regression'-Function
[20250403_172142.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172142.]: Entered 'cubic_regression'-Function
[20250403_172142.]: 'cubic_regression': minmax = FALSE
[20250403_172144.]: Entered 'clean_dt'-Function
[20250403_172144.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172144.]: got experimental data
[20250403_172144.]: Entered 'clean_dt'-Function
[20250403_172144.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172144.]: got calibration data
[20250403_172144.]: ### Starting with regression calculations ###
[20250403_172144.]: Entered 'regression_type1'-Function
[20250403_172144.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172144.]: Logging df_agg: CpG#1
[20250403_172144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172144.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172144.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172144.]: Entered 'hyperbolic_regression'-Function
[20250403_172144.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172145.]: Logging df_agg: CpG#2
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172145.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172145.]: Logging df_agg: CpG#3
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172145.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172145.]: Logging df_agg: CpG#4
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172145.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172145.]: Logging df_agg: CpG#5
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172145.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172145.]: Logging df_agg: CpG#6
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172145.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172145.]: Logging df_agg: CpG#7
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172145.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172145.]: Entered 'cubic_regression'-Function
[20250403_172145.]: 'cubic_regression': minmax = FALSE
[20250403_172145.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172145.]: Logging df_agg: CpG#8
[20250403_172145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172145.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172145.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172145.]: Entered 'hyperbolic_regression'-Function
[20250403_172145.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172146.]: Entered 'cubic_regression'-Function
[20250403_172146.]: 'cubic_regression': minmax = FALSE
[20250403_172146.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172146.]: Logging df_agg: CpG#9
[20250403_172146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172146.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172146.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172146.]: Entered 'hyperbolic_regression'-Function
[20250403_172146.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172146.]: Entered 'cubic_regression'-Function
[20250403_172146.]: 'cubic_regression': minmax = FALSE
[20250403_172146.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172146.]: Logging df_agg: row_means
[20250403_172146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172146.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172146.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172146.]: Entered 'hyperbolic_regression'-Function
[20250403_172146.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172146.]: Entered 'cubic_regression'-Function
[20250403_172146.]: 'cubic_regression': minmax = FALSE
[20250403_172148.]: Entered 'regression_type1'-Function
[20250403_172149.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172149.]: Logging df_agg: CpG#1
[20250403_172149.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172149.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172149.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172149.]: Entered 'hyperbolic_regression'-Function
[20250403_172149.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172149.]: Entered 'cubic_regression'-Function
[20250403_172149.]: 'cubic_regression': minmax = FALSE
[20250403_172149.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172149.]: Logging df_agg: CpG#2
[20250403_172149.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172149.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172149.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172149.]: Entered 'hyperbolic_regression'-Function
[20250403_172149.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172149.]: Entered 'cubic_regression'-Function
[20250403_172149.]: 'cubic_regression': minmax = FALSE
[20250403_172149.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172149.]: Logging df_agg: CpG#3
[20250403_172149.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172149.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172149.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172149.]: Entered 'hyperbolic_regression'-Function
[20250403_172149.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172150.]: Logging df_agg: CpG#4
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172150.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172150.]: Logging df_agg: CpG#5
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172150.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172149.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172150.]: Logging df_agg: CpG#6
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172150.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172150.]: Logging df_agg: CpG#7
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172150.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172150.]: Logging df_agg: CpG#8
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172150.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172150.]: Logging df_agg: CpG#9
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172150.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172150.]: Entered 'cubic_regression'-Function
[20250403_172150.]: 'cubic_regression': minmax = FALSE
[20250403_172150.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172150.]: Logging df_agg: row_means
[20250403_172150.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172150.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172150.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172150.]: Entered 'hyperbolic_regression'-Function
[20250403_172150.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172151.]: Entered 'cubic_regression'-Function
[20250403_172151.]: 'cubic_regression': minmax = FALSE
[20250403_172151.]: Entered 'solving_equations'-Function
[20250403_172151.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250403_172151.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250403_172151.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250403_172151.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250403_172151.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250403_172151.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250403_172151.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250403_172151.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250403_172151.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250403_172151.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250403_172151.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250403_172151.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250403_172151.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250403_172151.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250403_172151.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250403_172151.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250403_172151.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250403_172151.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250403_172151.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250403_172151.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250403_172151.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250403_172151.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250403_172151.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250403_172152.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250403_172152.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250403_172152.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250403_172152.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250403_172152.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250403_172152.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250403_172152.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250403_172152.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250403_172152.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250403_172152.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250403_172152.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250403_172152.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250403_172152.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250403_172152.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250403_172152.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250403_172152.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250403_172152.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250403_172152.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250403_172152.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250403_172152.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250403_172152.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250403_172152.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250403_172152.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250403_172152.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250403_172152.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250403_172152.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250403_172152.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250403_172152.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250403_172152.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250403_172152.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250403_172152.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250403_172152.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250403_172152.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250403_172152.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250403_172152.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250403_172152.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250403_172152.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250403_172152.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250403_172152.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250403_172152.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250403_172152.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250403_172152.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250403_172152.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250403_172152.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250403_172152.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250403_172152.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250403_172152.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250403_172152.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250403_172152.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250403_172152.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250403_172152.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250403_172152.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250403_172152.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250403_172152.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250403_172152.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250403_172152.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250403_172152.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250403_172152.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250403_172152.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250403_172152.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250403_172152.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250403_172152.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250403_172152.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250403_172152.]: ### Starting with regression calculations ###
[20250403_172152.]: Entered 'regression_type1'-Function
[20250403_172152.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250403_172153.]: Logging df_agg: CpG#1
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172153.]: Entered 'cubic_regression'-Function
[20250403_172153.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250403_172153.]: Logging df_agg: CpG#2
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172153.]: Entered 'cubic_regression'-Function
[20250403_172153.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250403_172153.]: Logging df_agg: CpG#3
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172153.]: Entered 'cubic_regression'-Function
[20250403_172153.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250403_172153.]: Logging df_agg: CpG#4
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172153.]: Entered 'cubic_regression'-Function
[20250403_172153.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250403_172153.]: Logging df_agg: CpG#5
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172154.]: Entered 'cubic_regression'-Function
[20250403_172154.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250403_172153.]: Logging df_agg: CpG#6
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172153.]: Entered 'cubic_regression'-Function
[20250403_172153.]: 'cubic_regression': minmax = FALSE
[20250403_172153.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250403_172153.]: Logging df_agg: CpG#7
[20250403_172153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172153.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250403_172153.]: Entered 'hyperbolic_regression'-Function
[20250403_172153.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172154.]: Entered 'cubic_regression'-Function
[20250403_172154.]: 'cubic_regression': minmax = FALSE
[20250403_172154.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250403_172154.]: Logging df_agg: CpG#8
[20250403_172154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172154.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250403_172154.]: Entered 'hyperbolic_regression'-Function
[20250403_172154.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172154.]: Entered 'cubic_regression'-Function
[20250403_172154.]: 'cubic_regression': minmax = FALSE
[20250403_172154.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250403_172154.]: Logging df_agg: CpG#9
[20250403_172154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172154.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250403_172154.]: Entered 'hyperbolic_regression'-Function
[20250403_172154.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172154.]: Entered 'cubic_regression'-Function
[20250403_172154.]: 'cubic_regression': minmax = FALSE
[20250403_172154.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250403_172154.]: Logging df_agg: row_means
[20250403_172154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172154.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250403_172154.]: Entered 'hyperbolic_regression'-Function
[20250403_172154.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172154.]: Entered 'cubic_regression'-Function
[20250403_172154.]: 'cubic_regression': minmax = FALSE
[20250403_172155.]: Entered 'solving_equations'-Function
[20250403_172155.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250403_172155.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250403_172155.]: ### Starting with regression calculations ###
[20250403_172155.]: Entered 'regression_type1'-Function
[20250403_172156.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250403_172156.]: Logging df_agg: CpG#1
[20250403_172156.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172156.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250403_172156.]: Entered 'hyperbolic_regression'-Function
[20250403_172156.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172157.]: Entered 'cubic_regression'-Function
[20250403_172157.]: 'cubic_regression': minmax = FALSE
[20250403_172157.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250403_172157.]: Logging df_agg: CpG#2
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172157.]: Entered 'cubic_regression'-Function
[20250403_172157.]: 'cubic_regression': minmax = FALSE
[20250403_172157.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250403_172157.]: Logging df_agg: CpG#3
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172157.]: Entered 'cubic_regression'-Function
[20250403_172157.]: 'cubic_regression': minmax = FALSE
[20250403_172157.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250403_172157.]: Logging df_agg: CpG#4
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172157.]: Entered 'cubic_regression'-Function
[20250403_172157.]: 'cubic_regression': minmax = FALSE
[20250403_172157.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250403_172157.]: Logging df_agg: CpG#5
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172158.]: Entered 'cubic_regression'-Function
[20250403_172158.]: 'cubic_regression': minmax = FALSE
[20250403_172156.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250403_172157.]: Logging df_agg: CpG#6
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172157.]: Entered 'cubic_regression'-Function
[20250403_172157.]: 'cubic_regression': minmax = FALSE
[20250403_172157.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250403_172157.]: Logging df_agg: CpG#7
[20250403_172157.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172157.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250403_172157.]: Entered 'hyperbolic_regression'-Function
[20250403_172157.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172158.]: Entered 'cubic_regression'-Function
[20250403_172158.]: 'cubic_regression': minmax = FALSE
[20250403_172158.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250403_172158.]: Logging df_agg: CpG#8
[20250403_172158.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172158.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250403_172158.]: Entered 'hyperbolic_regression'-Function
[20250403_172158.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172158.]: Entered 'cubic_regression'-Function
[20250403_172158.]: 'cubic_regression': minmax = FALSE
[20250403_172158.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250403_172158.]: Logging df_agg: CpG#9
[20250403_172158.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172158.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250403_172158.]: Entered 'hyperbolic_regression'-Function
[20250403_172158.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172158.]: Entered 'cubic_regression'-Function
[20250403_172158.]: 'cubic_regression': minmax = FALSE
[20250403_172158.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250403_172158.]: Logging df_agg: row_means
[20250403_172158.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172158.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250403_172158.]: Entered 'hyperbolic_regression'-Function
[20250403_172158.]: 'hyperbolic_regression': minmax = FALSE
[20250403_172158.]: Entered 'cubic_regression'-Function
[20250403_172158.]: 'cubic_regression': minmax = FALSE
[20250403_172159.]: Entered 'solving_equations'-Function
[20250403_172159.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250403_172159.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250403_172159.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250403_172159.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250403_172159.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250403_172159.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250403_172159.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250403_172159.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250403_172159.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250403_172159.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250403_172159.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250403_172159.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250403_172159.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250403_172159.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250403_172159.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250403_172159.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250403_172159.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250403_172159.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250403_172159.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250403_172159.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250403_172159.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250403_172159.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250403_172159.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250403_172159.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250403_172159.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250403_172159.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250403_172159.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250403_172159.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250403_172159.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250403_172159.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250403_172159.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250403_172159.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250403_172159.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250403_172159.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250403_172159.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172159.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250403_172159.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250403_172159.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250403_172159.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250403_172159.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250403_172159.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250403_172159.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250403_172159.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250403_172159.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250403_172159.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250403_172159.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250403_172159.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250403_172159.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250403_172159.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250403_172159.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250403_172159.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250403_172159.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250403_172159.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250403_172159.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250403_172159.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250403_172159.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250403_172159.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250403_172159.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250403_172159.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172159.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250403_172159.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172159.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250403_172159.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250403_172159.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250403_172159.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250403_172159.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250403_172159.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250403_172159.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250403_172159.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250403_172159.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250403_172159.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250403_172159.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250403_172159.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250403_172159.]: Entered 'solving_equations'-Function
[20250403_172159.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250403_172159.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250403_172159.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250403_172159.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250403_172159.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250403_172159.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250403_172159.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250403_172159.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250403_172159.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250403_172159.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250403_172159.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250403_172159.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250403_172159.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250403_172159.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250403_172159.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250403_172159.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250403_172159.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250403_172159.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250403_172200.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250403_172200.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250403_172200.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250403_172200.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250403_172200.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250403_172200.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250403_172200.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250403_172200.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250403_172200.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250403_172200.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250403_172200.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250403_172200.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250403_172200.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250403_172200.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250403_172200.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250403_172200.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250403_172200.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250403_172200.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250403_172200.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250403_172200.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250403_172200.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250403_172200.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250403_172200.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250403_172200.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250403_172200.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250403_172200.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250403_172200.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250403_172200.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250403_172200.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250403_172200.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250403_172200.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250403_172200.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250403_172200.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250403_172200.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250403_172200.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250403_172200.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250403_172200.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250403_172201.]: Entered 'clean_dt'-Function
[20250403_172201.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172201.]: got experimental data
[20250403_172201.]: Entered 'clean_dt'-Function
[20250403_172201.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172201.]: got calibration data
[20250403_172201.]: ### Starting with regression calculations ###
[20250403_172201.]: Entered 'regression_type1'-Function
[20250403_172201.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172201.]: Logging df_agg: CpG#1
[20250403_172201.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172201.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172201.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172201.]: Entered 'hyperbolic_regression'-Function
[20250403_172201.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172201.]: Entered 'cubic_regression'-Function
[20250403_172201.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172201.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172201.]: Logging df_agg: CpG#2
[20250403_172201.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172201.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172201.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172201.]: Entered 'hyperbolic_regression'-Function
[20250403_172201.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: Entered 'cubic_regression'-Function
[20250403_172202.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172202.]: Logging df_agg: CpG#3
[20250403_172202.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172202.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172202.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172202.]: Entered 'hyperbolic_regression'-Function
[20250403_172202.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: Entered 'cubic_regression'-Function
[20250403_172202.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172203.]: Logging df_agg: CpG#4
[20250403_172203.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172203.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172203.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172203.]: Entered 'hyperbolic_regression'-Function
[20250403_172203.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: Entered 'cubic_regression'-Function
[20250403_172203.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172203.]: Logging df_agg: CpG#5
[20250403_172203.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172203.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172203.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172203.]: Entered 'hyperbolic_regression'-Function
[20250403_172203.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: Entered 'cubic_regression'-Function
[20250403_172203.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172202.]: Logging df_agg: CpG#6
[20250403_172202.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172202.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172202.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172202.]: Entered 'hyperbolic_regression'-Function
[20250403_172202.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: Entered 'cubic_regression'-Function
[20250403_172202.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172202.]: Logging df_agg: CpG#7
[20250403_172202.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172202.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172202.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172202.]: Entered 'hyperbolic_regression'-Function
[20250403_172202.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: Entered 'cubic_regression'-Function
[20250403_172202.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172202.]: Logging df_agg: CpG#8
[20250403_172202.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172202.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172202.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172202.]: Entered 'hyperbolic_regression'-Function
[20250403_172202.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172202.]: Entered 'cubic_regression'-Function
[20250403_172202.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172203.]: Logging df_agg: CpG#9
[20250403_172203.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172203.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172203.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172203.]: Entered 'hyperbolic_regression'-Function
[20250403_172203.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: Entered 'cubic_regression'-Function
[20250403_172203.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172203.]: Logging df_agg: row_means
[20250403_172203.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172203.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172203.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172203.]: Entered 'hyperbolic_regression'-Function
[20250403_172203.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172203.]: Entered 'cubic_regression'-Function
[20250403_172203.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172205.]: Entered 'regression_type1'-Function
[20250403_172206.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172207.]: Logging df_agg: CpG#1
[20250403_172207.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172207.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172207.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172207.]: Entered 'hyperbolic_regression'-Function
[20250403_172207.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: Entered 'cubic_regression'-Function
[20250403_172207.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172207.]: Logging df_agg: CpG#2
[20250403_172207.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172207.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172207.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172207.]: Entered 'hyperbolic_regression'-Function
[20250403_172207.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: Entered 'cubic_regression'-Function
[20250403_172207.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172207.]: Logging df_agg: CpG#3
[20250403_172207.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172207.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172207.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172207.]: Entered 'hyperbolic_regression'-Function
[20250403_172207.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: Entered 'cubic_regression'-Function
[20250403_172207.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172208.]: Logging df_agg: CpG#4
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172208.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: Entered 'cubic_regression'-Function
[20250403_172208.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172208.]: Logging df_agg: CpG#5
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172208.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: Entered 'cubic_regression'-Function
[20250403_172208.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172207.]: Logging df_agg: CpG#6
[20250403_172207.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172207.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172207.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172207.]: Entered 'hyperbolic_regression'-Function
[20250403_172207.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172207.]: Entered 'cubic_regression'-Function
[20250403_172207.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172208.]: Logging df_agg: CpG#7
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172208.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: Entered 'cubic_regression'-Function
[20250403_172208.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172208.]: Logging df_agg: CpG#8
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172208.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: Entered 'cubic_regression'-Function
[20250403_172208.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172208.]: Logging df_agg: CpG#9
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172208.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: Entered 'cubic_regression'-Function
[20250403_172208.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172208.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172208.]: Logging df_agg: row_means
[20250403_172208.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172208.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172208.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172208.]: Entered 'hyperbolic_regression'-Function
[20250403_172208.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172209.]: Entered 'cubic_regression'-Function
[20250403_172209.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172210.]: Entered 'clean_dt'-Function
[20250403_172210.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172210.]: got experimental data
[20250403_172210.]: Entered 'clean_dt'-Function
[20250403_172210.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172210.]: got calibration data
[20250403_172210.]: ### Starting with regression calculations ###
[20250403_172210.]: Entered 'regression_type1'-Function
[20250403_172211.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172211.]: Logging df_agg: CpG#1
[20250403_172211.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172211.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172211.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172211.]: Entered 'hyperbolic_regression'-Function
[20250403_172211.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: Entered 'cubic_regression'-Function
[20250403_172211.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172211.]: Logging df_agg: CpG#2
[20250403_172211.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172211.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172211.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172211.]: Entered 'hyperbolic_regression'-Function
[20250403_172211.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: Entered 'cubic_regression'-Function
[20250403_172211.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172211.]: Logging df_agg: CpG#3
[20250403_172211.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172211.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172211.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172211.]: Entered 'hyperbolic_regression'-Function
[20250403_172211.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172212.]: Logging df_agg: CpG#4
[20250403_172212.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172212.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172212.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172212.]: Entered 'hyperbolic_regression'-Function
[20250403_172212.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172212.]: Logging df_agg: CpG#5
[20250403_172212.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172212.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172212.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172212.]: Entered 'hyperbolic_regression'-Function
[20250403_172212.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172211.]: Logging df_agg: CpG#6
[20250403_172211.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172211.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172211.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172211.]: Entered 'hyperbolic_regression'-Function
[20250403_172211.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: Entered 'cubic_regression'-Function
[20250403_172211.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172211.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172211.]: Logging df_agg: CpG#7
[20250403_172211.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172211.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172211.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172211.]: Entered 'hyperbolic_regression'-Function
[20250403_172211.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172212.]: Logging df_agg: CpG#8
[20250403_172212.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172212.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172212.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172212.]: Entered 'hyperbolic_regression'-Function
[20250403_172212.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172212.]: Logging df_agg: CpG#9
[20250403_172212.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172212.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172212.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172212.]: Entered 'hyperbolic_regression'-Function
[20250403_172212.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: Entered 'cubic_regression'-Function
[20250403_172212.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172212.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172212.]: Logging df_agg: row_means
[20250403_172212.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172212.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172212.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172212.]: Entered 'hyperbolic_regression'-Function
[20250403_172212.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172213.]: Entered 'cubic_regression'-Function
[20250403_172213.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172214.]: Entered 'regression_type1'-Function
[20250403_172215.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172215.]: Logging df_agg: CpG#1
[20250403_172215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172215.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250403_172215.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250403_172215.]: Entered 'hyperbolic_regression'-Function
[20250403_172215.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: Entered 'cubic_regression'-Function
[20250403_172216.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172216.]: Logging df_agg: CpG#2
[20250403_172216.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172216.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250403_172216.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250403_172216.]: Entered 'hyperbolic_regression'-Function
[20250403_172216.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: Entered 'cubic_regression'-Function
[20250403_172216.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172216.]: Logging df_agg: CpG#3
[20250403_172216.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172216.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250403_172216.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250403_172216.]: Entered 'hyperbolic_regression'-Function
[20250403_172216.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: Entered 'cubic_regression'-Function
[20250403_172216.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172216.]: Logging df_agg: CpG#4
[20250403_172216.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172216.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250403_172216.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250403_172216.]: Entered 'hyperbolic_regression'-Function
[20250403_172216.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: Entered 'cubic_regression'-Function
[20250403_172216.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172216.]: Logging df_agg: CpG#5
[20250403_172216.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172216.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250403_172216.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250403_172216.]: Entered 'hyperbolic_regression'-Function
[20250403_172216.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: Entered 'cubic_regression'-Function
[20250403_172217.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172216.]: Logging df_agg: CpG#6
[20250403_172216.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172216.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250403_172216.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250403_172216.]: Entered 'hyperbolic_regression'-Function
[20250403_172216.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172216.]: Entered 'cubic_regression'-Function
[20250403_172216.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172217.]: Logging df_agg: CpG#7
[20250403_172217.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172217.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250403_172217.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250403_172217.]: Entered 'hyperbolic_regression'-Function
[20250403_172217.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: Entered 'cubic_regression'-Function
[20250403_172217.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172217.]: Logging df_agg: CpG#8
[20250403_172217.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172217.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250403_172217.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250403_172217.]: Entered 'hyperbolic_regression'-Function
[20250403_172217.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: Entered 'cubic_regression'-Function
[20250403_172217.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172217.]: Logging df_agg: CpG#9
[20250403_172217.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172217.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250403_172217.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250403_172217.]: Entered 'hyperbolic_regression'-Function
[20250403_172217.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: Entered 'cubic_regression'-Function
[20250403_172217.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172217.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172217.]: Logging df_agg: row_means
[20250403_172217.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172217.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250403_172217.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250403_172217.]: Entered 'hyperbolic_regression'-Function
[20250403_172217.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172218.]: Entered 'cubic_regression'-Function
[20250403_172218.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172219.]: Entered 'solving_equations'-Function
[20250403_172219.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250403_172219.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250403_172219.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250403_172219.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250403_172219.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250403_172219.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250403_172219.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250403_172219.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250403_172219.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250403_172219.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250403_172219.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250403_172219.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250403_172219.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250403_172219.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250403_172219.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250403_172219.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250403_172219.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250403_172219.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250403_172219.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250403_172219.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250403_172219.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250403_172219.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250403_172219.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250403_172219.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250403_172219.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250403_172219.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250403_172219.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250403_172219.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250403_172219.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250403_172219.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250403_172219.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250403_172219.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250403_172219.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250403_172219.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250403_172219.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250403_172219.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250403_172219.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250403_172219.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250403_172219.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250403_172219.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250403_172219.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250403_172219.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250403_172219.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250403_172219.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250403_172219.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250403_172219.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250403_172219.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250403_172219.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250403_172219.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250403_172219.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250403_172219.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250403_172219.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250403_172219.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250403_172219.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250403_172219.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250403_172219.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250403_172219.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250403_172219.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250403_172219.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250403_172219.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250403_172219.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250403_172219.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250403_172219.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250403_172219.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250403_172219.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250403_172219.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250403_172219.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250403_172219.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250403_172219.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250403_172219.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250403_172219.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250403_172219.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172219.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172219.]: ### Starting with regression calculations ###
[20250403_172219.]: Entered 'regression_type1'-Function
[20250403_172220.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250403_172220.]: Logging df_agg: CpG#1
[20250403_172220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172220.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250403_172220.]: Entered 'hyperbolic_regression'-Function
[20250403_172220.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: Entered 'cubic_regression'-Function
[20250403_172220.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250403_172220.]: Logging df_agg: CpG#2
[20250403_172220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172220.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250403_172220.]: Entered 'hyperbolic_regression'-Function
[20250403_172220.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: Entered 'cubic_regression'-Function
[20250403_172220.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250403_172220.]: Logging df_agg: CpG#3
[20250403_172220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172220.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250403_172220.]: Entered 'hyperbolic_regression'-Function
[20250403_172220.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: Entered 'cubic_regression'-Function
[20250403_172220.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250403_172221.]: Logging df_agg: CpG#4
[20250403_172221.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172221.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250403_172221.]: Entered 'hyperbolic_regression'-Function
[20250403_172221.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: Entered 'cubic_regression'-Function
[20250403_172221.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250403_172221.]: Logging df_agg: CpG#5
[20250403_172221.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172221.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250403_172221.]: Entered 'hyperbolic_regression'-Function
[20250403_172221.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: Entered 'cubic_regression'-Function
[20250403_172221.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250403_172220.]: Logging df_agg: CpG#6
[20250403_172220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172220.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250403_172220.]: Entered 'hyperbolic_regression'-Function
[20250403_172220.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: Entered 'cubic_regression'-Function
[20250403_172220.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172220.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250403_172220.]: Logging df_agg: CpG#7
[20250403_172220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172220.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250403_172220.]: Entered 'hyperbolic_regression'-Function
[20250403_172220.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: Entered 'cubic_regression'-Function
[20250403_172221.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250403_172221.]: Logging df_agg: CpG#8
[20250403_172221.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172221.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250403_172221.]: Entered 'hyperbolic_regression'-Function
[20250403_172221.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: Entered 'cubic_regression'-Function
[20250403_172221.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250403_172221.]: Logging df_agg: CpG#9
[20250403_172221.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172221.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250403_172221.]: Entered 'hyperbolic_regression'-Function
[20250403_172221.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: Entered 'cubic_regression'-Function
[20250403_172221.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172221.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250403_172221.]: Logging df_agg: row_means
[20250403_172221.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172221.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250403_172221.]: Entered 'hyperbolic_regression'-Function
[20250403_172221.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172222.]: Entered 'cubic_regression'-Function
[20250403_172222.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172223.]: Entered 'solving_equations'-Function
[20250403_172223.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172223.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250403_172223.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172223.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250403_172223.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250403_172223.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172223.]: ### Starting with regression calculations ###
[20250403_172223.]: Entered 'regression_type1'-Function
[20250403_172224.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250403_172224.]: Logging df_agg: CpG#1
[20250403_172224.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172224.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250403_172224.]: Entered 'hyperbolic_regression'-Function
[20250403_172224.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172224.]: Entered 'cubic_regression'-Function
[20250403_172224.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172224.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250403_172224.]: Logging df_agg: CpG#2
[20250403_172224.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172224.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250403_172224.]: Entered 'hyperbolic_regression'-Function
[20250403_172224.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172224.]: Entered 'cubic_regression'-Function
[20250403_172224.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172224.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250403_172224.]: Logging df_agg: CpG#3
[20250403_172224.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172224.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250403_172224.]: Entered 'hyperbolic_regression'-Function
[20250403_172224.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250403_172225.]: Logging df_agg: CpG#4
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250403_172225.]: Logging df_agg: CpG#5
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172224.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250403_172224.]: Logging df_agg: CpG#6
[20250403_172224.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172224.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250403_172224.]: Entered 'hyperbolic_regression'-Function
[20250403_172224.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250403_172225.]: Logging df_agg: CpG#7
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250403_172225.]: Logging df_agg: CpG#8
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250403_172225.]: Logging df_agg: CpG#9
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: Entered 'cubic_regression'-Function
[20250403_172225.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172225.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250403_172225.]: Logging df_agg: row_means
[20250403_172225.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250403_172225.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250403_172225.]: Entered 'hyperbolic_regression'-Function
[20250403_172225.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172226.]: Entered 'cubic_regression'-Function
[20250403_172226.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250403_172226.]: Entered 'solving_equations'-Function
[20250403_172226.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250403_172226.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250403_172226.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250403_172227.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250403_172227.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250403_172227.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250403_172227.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250403_172227.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250403_172227.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250403_172227.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250403_172227.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250403_172227.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250403_172227.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250403_172227.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250403_172227.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250403_172227.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250403_172227.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250403_172227.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250403_172227.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250403_172227.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250403_172227.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250403_172227.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250403_172227.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250403_172227.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250403_172227.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250403_172227.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250403_172227.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250403_172227.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250403_172227.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250403_172227.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250403_172227.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250403_172227.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250403_172227.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250403_172227.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250403_172227.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250403_172227.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250403_172227.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250403_172227.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250403_172227.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250403_172227.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250403_172227.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250403_172227.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250403_172227.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250403_172227.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250403_172227.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250403_172227.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250403_172227.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250403_172227.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250403_172227.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250403_172227.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250403_172227.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250403_172227.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250403_172227.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250403_172227.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250403_172227.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250403_172227.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250403_172227.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250403_172227.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250403_172227.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250403_172227.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250403_172227.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250403_172227.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250403_172227.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250403_172227.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250403_172227.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250403_172227.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250403_172227.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250403_172227.]: Entered 'solving_equations'-Function
[20250403_172227.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250403_172227.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250403_172227.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250403_172227.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250403_172227.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250403_172227.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250403_172227.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250403_172227.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250403_172227.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250403_172227.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250403_172227.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250403_172227.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250403_172227.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250403_172227.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250403_172227.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250403_172227.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250403_172227.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250403_172227.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250403_172227.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250403_172227.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250403_172227.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250403_172227.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250403_172227.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250403_172227.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250403_172227.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250403_172227.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250403_172227.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250403_172227.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250403_172227.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250403_172227.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250403_172227.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250403_172227.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250403_172227.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250403_172227.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250403_172227.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250403_172227.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250403_172227.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250403_172227.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250403_172227.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250403_172227.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250403_172227.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250403_172227.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250403_172227.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250403_172227.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250403_172227.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250403_172227.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250403_172227.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250403_172227.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250403_172227.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250403_172227.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250403_172227.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250403_172227.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250403_172227.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250403_172227.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250403_172227.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250403_172227.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250403_172227.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250403_172306.]: Entered 'clean_dt'-Function
[20250403_172306.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172306.]: got experimental data
[20250403_172306.]: Entered 'clean_dt'-Function
[20250403_172306.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250403_172306.]: got experimental data
[20250403_172307.]: Entered 'clean_dt'-Function
[20250403_172307.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172307.]: got calibration data
[20250403_172307.]: Entered 'clean_dt'-Function
[20250403_172307.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250403_172307.]: got calibration data
[20250403_172307.]: Entered 'hyperbolic_regression'-Function
[20250403_172307.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [4m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250402_080737.]: Entered 'clean_dt'-Function
[20250402_080737.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080737.]: got experimental data
[20250402_080737.]: Entered 'clean_dt'-Function
[20250402_080737.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080737.]: got calibration data
[20250402_080737.]: ### Starting with regression calculations ###
[20250402_080737.]: Entered 'regression_type1'-Function
[20250402_080739.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080739.]: Logging df_agg: CpG#1
[20250402_080739.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080739.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_080739.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080739.]: Entered 'hyperbolic_regression'-Function
[20250402_080739.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080741.]: Entered 'cubic_regression'-Function
[20250402_080741.]: 'cubic_regression': minmax = FALSE
[20250402_080741.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080741.]: Logging df_agg: CpG#2
[20250402_080741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080741.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_080741.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080741.]: Entered 'hyperbolic_regression'-Function
[20250402_080741.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080742.]: Entered 'cubic_regression'-Function
[20250402_080742.]: 'cubic_regression': minmax = FALSE
[20250402_080742.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080742.]: Logging df_agg: CpG#3
[20250402_080742.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080742.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_080742.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080742.]: Entered 'hyperbolic_regression'-Function
[20250402_080742.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080743.]: Entered 'cubic_regression'-Function
[20250402_080743.]: 'cubic_regression': minmax = FALSE
[20250402_080743.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080743.]: Logging df_agg: CpG#4
[20250402_080743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080743.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_080743.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080743.]: Entered 'hyperbolic_regression'-Function
[20250402_080743.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080745.]: Entered 'cubic_regression'-Function
[20250402_080745.]: 'cubic_regression': minmax = FALSE
[20250402_080745.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080745.]: Logging df_agg: CpG#5
[20250402_080745.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080745.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_080745.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080745.]: Entered 'hyperbolic_regression'-Function
[20250402_080745.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080746.]: Entered 'cubic_regression'-Function
[20250402_080746.]: 'cubic_regression': minmax = FALSE
[20250402_080743.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080743.]: Logging df_agg: CpG#6
[20250402_080743.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080743.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_080743.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080743.]: Entered 'hyperbolic_regression'-Function
[20250402_080743.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080744.]: Entered 'cubic_regression'-Function
[20250402_080744.]: 'cubic_regression': minmax = FALSE
[20250402_080744.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080744.]: Logging df_agg: CpG#7
[20250402_080744.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080744.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_080744.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080744.]: Entered 'hyperbolic_regression'-Function
[20250402_080744.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080745.]: Entered 'cubic_regression'-Function
[20250402_080745.]: 'cubic_regression': minmax = FALSE
[20250402_080746.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080746.]: Logging df_agg: CpG#8
[20250402_080746.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080746.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_080746.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080746.]: Entered 'hyperbolic_regression'-Function
[20250402_080746.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080747.]: Entered 'cubic_regression'-Function
[20250402_080747.]: 'cubic_regression': minmax = FALSE
[20250402_080747.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080747.]: Logging df_agg: CpG#9
[20250402_080747.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080747.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_080747.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080747.]: Entered 'hyperbolic_regression'-Function
[20250402_080747.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080748.]: Entered 'cubic_regression'-Function
[20250402_080748.]: 'cubic_regression': minmax = FALSE
[20250402_080748.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080748.]: Logging df_agg: row_means
[20250402_080748.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080748.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_080748.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080748.]: Entered 'hyperbolic_regression'-Function
[20250402_080748.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080749.]: Entered 'cubic_regression'-Function
[20250402_080749.]: 'cubic_regression': minmax = FALSE
[20250402_080801.]: Entered 'regression_type1'-Function
[20250402_080803.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080804.]: Logging df_agg: CpG#1
[20250402_080804.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080804.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_080804.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080804.]: Entered 'hyperbolic_regression'-Function
[20250402_080804.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080804.]: Entered 'cubic_regression'-Function
[20250402_080805.]: 'cubic_regression': minmax = FALSE
[20250402_080805.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080805.]: Logging df_agg: CpG#2
[20250402_080805.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080805.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_080805.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080805.]: Entered 'hyperbolic_regression'-Function
[20250402_080805.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080805.]: Entered 'cubic_regression'-Function
[20250402_080805.]: 'cubic_regression': minmax = FALSE
[20250402_080805.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080805.]: Logging df_agg: CpG#3
[20250402_080805.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080805.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_080805.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080805.]: Entered 'hyperbolic_regression'-Function
[20250402_080805.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080806.]: Entered 'cubic_regression'-Function
[20250402_080806.]: 'cubic_regression': minmax = FALSE
[20250402_080806.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080806.]: Logging df_agg: CpG#4
[20250402_080806.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080806.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_080806.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080806.]: Entered 'hyperbolic_regression'-Function
[20250402_080806.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080807.]: Entered 'cubic_regression'-Function
[20250402_080807.]: 'cubic_regression': minmax = FALSE
[20250402_080807.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080807.]: Logging df_agg: CpG#5
[20250402_080807.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080807.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_080807.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080807.]: Entered 'hyperbolic_regression'-Function
[20250402_080807.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080807.]: Entered 'cubic_regression'-Function
[20250402_080807.]: 'cubic_regression': minmax = FALSE
[20250402_080803.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080805.]: Logging df_agg: CpG#6
[20250402_080805.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080805.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_080805.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080805.]: Entered 'hyperbolic_regression'-Function
[20250402_080805.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080807.]: Entered 'cubic_regression'-Function
[20250402_080807.]: 'cubic_regression': minmax = FALSE
[20250402_080807.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080807.]: Logging df_agg: CpG#7
[20250402_080807.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080807.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_080807.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080807.]: Entered 'hyperbolic_regression'-Function
[20250402_080807.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080809.]: Entered 'cubic_regression'-Function
[20250402_080809.]: 'cubic_regression': minmax = FALSE
[20250402_080809.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080810.]: Logging df_agg: CpG#8
[20250402_080810.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080810.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_080810.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080810.]: Entered 'hyperbolic_regression'-Function
[20250402_080810.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080811.]: Entered 'cubic_regression'-Function
[20250402_080811.]: 'cubic_regression': minmax = FALSE
[20250402_080811.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080811.]: Logging df_agg: CpG#9
[20250402_080811.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080811.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_080811.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080811.]: Entered 'hyperbolic_regression'-Function
[20250402_080811.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080812.]: Entered 'cubic_regression'-Function
[20250402_080812.]: 'cubic_regression': minmax = FALSE
[20250402_080812.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080813.]: Logging df_agg: row_means
[20250402_080813.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080813.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_080813.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080813.]: Entered 'hyperbolic_regression'-Function
[20250402_080813.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080814.]: Entered 'cubic_regression'-Function
[20250402_080814.]: 'cubic_regression': minmax = FALSE
[20250402_080818.]: Entered 'clean_dt'-Function
[20250402_080818.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080818.]: got experimental data
[20250402_080818.]: Entered 'clean_dt'-Function
[20250402_080818.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080818.]: got calibration data
[20250402_080819.]: ### Starting with regression calculations ###
[20250402_080819.]: Entered 'regression_type1'-Function
[20250402_080820.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080820.]: Logging df_agg: CpG#1
[20250402_080820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080820.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_080820.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080820.]: Entered 'hyperbolic_regression'-Function
[20250402_080820.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080821.]: Entered 'cubic_regression'-Function
[20250402_080822.]: 'cubic_regression': minmax = FALSE
[20250402_080822.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080822.]: Logging df_agg: CpG#2
[20250402_080822.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080822.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_080822.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080822.]: Entered 'hyperbolic_regression'-Function
[20250402_080822.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080824.]: Entered 'cubic_regression'-Function
[20250402_080824.]: 'cubic_regression': minmax = FALSE
[20250402_080824.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080824.]: Logging df_agg: CpG#3
[20250402_080824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080824.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_080824.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080824.]: Entered 'hyperbolic_regression'-Function
[20250402_080824.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080825.]: Entered 'cubic_regression'-Function
[20250402_080825.]: 'cubic_regression': minmax = FALSE
[20250402_080825.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080825.]: Logging df_agg: CpG#4
[20250402_080825.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080825.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_080825.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080825.]: Entered 'hyperbolic_regression'-Function
[20250402_080825.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080826.]: Entered 'cubic_regression'-Function
[20250402_080826.]: 'cubic_regression': minmax = FALSE
[20250402_080826.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080826.]: Logging df_agg: CpG#5
[20250402_080826.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080826.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_080826.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080826.]: Entered 'hyperbolic_regression'-Function
[20250402_080827.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080828.]: Entered 'cubic_regression'-Function
[20250402_080828.]: 'cubic_regression': minmax = FALSE
[20250402_080824.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080824.]: Logging df_agg: CpG#6
[20250402_080824.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080824.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_080824.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080824.]: Entered 'hyperbolic_regression'-Function
[20250402_080824.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080825.]: Entered 'cubic_regression'-Function
[20250402_080825.]: 'cubic_regression': minmax = FALSE
[20250402_080825.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080825.]: Logging df_agg: CpG#7
[20250402_080825.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080825.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_080825.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080825.]: Entered 'hyperbolic_regression'-Function
[20250402_080825.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080826.]: Entered 'cubic_regression'-Function
[20250402_080826.]: 'cubic_regression': minmax = FALSE
[20250402_080826.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080826.]: Logging df_agg: CpG#8
[20250402_080826.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080826.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_080826.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080826.]: Entered 'hyperbolic_regression'-Function
[20250402_080826.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080828.]: Entered 'cubic_regression'-Function
[20250402_080828.]: 'cubic_regression': minmax = FALSE
[20250402_080828.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080828.]: Logging df_agg: CpG#9
[20250402_080828.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080828.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_080828.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080828.]: Entered 'hyperbolic_regression'-Function
[20250402_080828.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080830.]: Entered 'cubic_regression'-Function
[20250402_080830.]: 'cubic_regression': minmax = FALSE
[20250402_080830.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080830.]: Logging df_agg: row_means
[20250402_080830.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080830.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_080830.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080830.]: Entered 'hyperbolic_regression'-Function
[20250402_080830.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080831.]: Entered 'cubic_regression'-Function
[20250402_080831.]: 'cubic_regression': minmax = FALSE
[20250402_080840.]: Entered 'regression_type1'-Function
[20250402_080843.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080844.]: Logging df_agg: CpG#1
[20250402_080844.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080844.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_080844.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080844.]: Entered 'hyperbolic_regression'-Function
[20250402_080844.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080845.]: Entered 'cubic_regression'-Function
[20250402_080845.]: 'cubic_regression': minmax = FALSE
[20250402_080845.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080845.]: Logging df_agg: CpG#2
[20250402_080845.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080845.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_080845.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080845.]: Entered 'hyperbolic_regression'-Function
[20250402_080845.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080846.]: Entered 'cubic_regression'-Function
[20250402_080846.]: 'cubic_regression': minmax = FALSE
[20250402_080846.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080846.]: Logging df_agg: CpG#3
[20250402_080847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080847.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_080847.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080847.]: Entered 'hyperbolic_regression'-Function
[20250402_080847.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080848.]: Entered 'cubic_regression'-Function
[20250402_080848.]: 'cubic_regression': minmax = FALSE
[20250402_080848.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080848.]: Logging df_agg: CpG#4
[20250402_080848.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080848.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_080848.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080848.]: Entered 'hyperbolic_regression'-Function
[20250402_080848.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080849.]: Entered 'cubic_regression'-Function
[20250402_080849.]: 'cubic_regression': minmax = FALSE
[20250402_080849.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080849.]: Logging df_agg: CpG#5
[20250402_080849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080849.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_080849.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080849.]: Entered 'hyperbolic_regression'-Function
[20250402_080849.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080851.]: Entered 'cubic_regression'-Function
[20250402_080851.]: 'cubic_regression': minmax = FALSE
[20250402_080844.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080845.]: Logging df_agg: CpG#6
[20250402_080845.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080845.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_080845.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080845.]: Entered 'hyperbolic_regression'-Function
[20250402_080845.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080846.]: Entered 'cubic_regression'-Function
[20250402_080846.]: 'cubic_regression': minmax = FALSE
[20250402_080846.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080846.]: Logging df_agg: CpG#7
[20250402_080846.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080846.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_080846.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080846.]: Entered 'hyperbolic_regression'-Function
[20250402_080846.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080847.]: Entered 'cubic_regression'-Function
[20250402_080847.]: 'cubic_regression': minmax = FALSE
[20250402_080847.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080847.]: Logging df_agg: CpG#8
[20250402_080847.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080847.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_080847.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080847.]: Entered 'hyperbolic_regression'-Function
[20250402_080847.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080849.]: Entered 'cubic_regression'-Function
[20250402_080849.]: 'cubic_regression': minmax = FALSE
[20250402_080849.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080849.]: Logging df_agg: CpG#9
[20250402_080849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080849.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_080849.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080849.]: Entered 'hyperbolic_regression'-Function
[20250402_080849.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080850.]: Entered 'cubic_regression'-Function
[20250402_080850.]: 'cubic_regression': minmax = FALSE
[20250402_080850.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080850.]: Logging df_agg: row_means
[20250402_080850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080850.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_080850.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080850.]: Entered 'hyperbolic_regression'-Function
[20250402_080850.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080851.]: Entered 'cubic_regression'-Function
[20250402_080851.]: 'cubic_regression': minmax = FALSE
[20250402_080854.]: Entered 'solving_equations'-Function
[20250402_080855.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250402_080856.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250402_080856.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250402_080856.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250402_080856.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250402_080856.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250402_080856.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250402_080856.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250402_080857.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250402_080857.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250402_080857.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250402_080857.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250402_080857.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250402_080857.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250402_080857.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250402_080857.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250402_080857.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250402_080857.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250402_080857.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250402_080857.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250402_080857.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250402_080857.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250402_080857.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250402_080857.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250402_080857.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250402_080857.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250402_080857.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250402_080857.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250402_080857.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250402_080857.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250402_080857.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250402_080857.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250402_080857.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250402_080857.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250402_080857.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250402_080857.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250402_080857.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250402_080857.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250402_080857.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250402_080857.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250402_080857.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250402_080857.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250402_080857.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250402_080857.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250402_080857.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250402_080857.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250402_080857.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250402_080857.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250402_080857.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250402_080857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250402_080857.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250402_080857.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250402_080858.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250402_080858.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250402_080858.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250402_080858.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250402_080858.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250402_080858.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250402_080858.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250402_080858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250402_080858.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250402_080858.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250402_080858.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250402_080858.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250402_080858.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250402_080858.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250402_080858.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250402_080858.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250402_080858.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250402_080858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250402_080858.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250402_080858.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250402_080858.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250402_080858.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250402_080858.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250402_080858.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250402_080858.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250402_080858.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250402_080858.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250402_080858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250402_080858.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250402_080858.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250402_080858.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250402_080858.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250402_080858.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250402_080858.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250402_080858.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250402_080858.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250402_080858.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250402_080858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250402_080858.]: ### Starting with regression calculations ###
[20250402_080858.]: Entered 'regression_type1'-Function
[20250402_080901.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250402_080901.]: Logging df_agg: CpG#1
[20250402_080901.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080901.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250402_080901.]: Entered 'hyperbolic_regression'-Function
[20250402_080902.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080903.]: Entered 'cubic_regression'-Function
[20250402_080903.]: 'cubic_regression': minmax = FALSE
[20250402_080903.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250402_080903.]: Logging df_agg: CpG#2
[20250402_080903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080903.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250402_080903.]: Entered 'hyperbolic_regression'-Function
[20250402_080903.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080904.]: Entered 'cubic_regression'-Function
[20250402_080904.]: 'cubic_regression': minmax = FALSE
[20250402_080905.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250402_080905.]: Logging df_agg: CpG#3
[20250402_080905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080905.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250402_080905.]: Entered 'hyperbolic_regression'-Function
[20250402_080905.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080906.]: Entered 'cubic_regression'-Function
[20250402_080906.]: 'cubic_regression': minmax = FALSE
[20250402_080906.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250402_080906.]: Logging df_agg: CpG#4
[20250402_080906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080906.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250402_080906.]: Entered 'hyperbolic_regression'-Function
[20250402_080906.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080907.]: Entered 'cubic_regression'-Function
[20250402_080908.]: 'cubic_regression': minmax = FALSE
[20250402_080908.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250402_080908.]: Logging df_agg: CpG#5
[20250402_080908.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080908.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250402_080908.]: Entered 'hyperbolic_regression'-Function
[20250402_080908.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080909.]: Entered 'cubic_regression'-Function
[20250402_080909.]: 'cubic_regression': minmax = FALSE
[20250402_080903.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250402_080903.]: Logging df_agg: CpG#6
[20250402_080903.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080903.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250402_080903.]: Entered 'hyperbolic_regression'-Function
[20250402_080903.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080905.]: Entered 'cubic_regression'-Function
[20250402_080905.]: 'cubic_regression': minmax = FALSE
[20250402_080905.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250402_080905.]: Logging df_agg: CpG#7
[20250402_080905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080905.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250402_080905.]: Entered 'hyperbolic_regression'-Function
[20250402_080905.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080906.]: Entered 'cubic_regression'-Function
[20250402_080906.]: 'cubic_regression': minmax = FALSE
[20250402_080906.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250402_080906.]: Logging df_agg: CpG#8
[20250402_080906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080906.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250402_080906.]: Entered 'hyperbolic_regression'-Function
[20250402_080906.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080908.]: Entered 'cubic_regression'-Function
[20250402_080908.]: 'cubic_regression': minmax = FALSE
[20250402_080908.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250402_080908.]: Logging df_agg: CpG#9
[20250402_080908.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080908.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250402_080908.]: Entered 'hyperbolic_regression'-Function
[20250402_080908.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080909.]: Entered 'cubic_regression'-Function
[20250402_080909.]: 'cubic_regression': minmax = FALSE
[20250402_080909.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250402_080909.]: Logging df_agg: row_means
[20250402_080909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080909.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250402_080909.]: Entered 'hyperbolic_regression'-Function
[20250402_080909.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080910.]: Entered 'cubic_regression'-Function
[20250402_080910.]: 'cubic_regression': minmax = FALSE
[20250402_080913.]: Entered 'solving_equations'-Function
[20250402_080913.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_080913.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250402_080913.]: ### Starting with regression calculations ###
[20250402_080913.]: Entered 'regression_type1'-Function
[20250402_080916.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250402_080916.]: Logging df_agg: CpG#1
[20250402_080916.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080916.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250402_080916.]: Entered 'hyperbolic_regression'-Function
[20250402_080916.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080918.]: Entered 'cubic_regression'-Function
[20250402_080918.]: 'cubic_regression': minmax = FALSE
[20250402_080918.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250402_080918.]: Logging df_agg: CpG#2
[20250402_080918.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080918.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250402_080918.]: Entered 'hyperbolic_regression'-Function
[20250402_080918.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080919.]: Entered 'cubic_regression'-Function
[20250402_080919.]: 'cubic_regression': minmax = FALSE
[20250402_080919.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250402_080919.]: Logging df_agg: CpG#3
[20250402_080919.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080919.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250402_080919.]: Entered 'hyperbolic_regression'-Function
[20250402_080919.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080920.]: Entered 'cubic_regression'-Function
[20250402_080920.]: 'cubic_regression': minmax = FALSE
[20250402_080920.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250402_080920.]: Logging df_agg: CpG#4
[20250402_080920.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080920.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250402_080920.]: Entered 'hyperbolic_regression'-Function
[20250402_080920.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080921.]: Entered 'cubic_regression'-Function
[20250402_080921.]: 'cubic_regression': minmax = FALSE
[20250402_080921.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250402_080921.]: Logging df_agg: CpG#5
[20250402_080921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080921.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250402_080921.]: Entered 'hyperbolic_regression'-Function
[20250402_080921.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080923.]: Entered 'cubic_regression'-Function
[20250402_080923.]: 'cubic_regression': minmax = FALSE
[20250402_080919.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250402_080920.]: Logging df_agg: CpG#6
[20250402_080920.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080920.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250402_080920.]: Entered 'hyperbolic_regression'-Function
[20250402_080920.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080922.]: Entered 'cubic_regression'-Function
[20250402_080922.]: 'cubic_regression': minmax = FALSE
[20250402_080922.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250402_080922.]: Logging df_agg: CpG#7
[20250402_080922.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080922.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250402_080922.]: Entered 'hyperbolic_regression'-Function
[20250402_080922.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080923.]: Entered 'cubic_regression'-Function
[20250402_080923.]: 'cubic_regression': minmax = FALSE
[20250402_080923.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250402_080923.]: Logging df_agg: CpG#8
[20250402_080923.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080923.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250402_080923.]: Entered 'hyperbolic_regression'-Function
[20250402_080923.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080924.]: Entered 'cubic_regression'-Function
[20250402_080924.]: 'cubic_regression': minmax = FALSE
[20250402_080924.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250402_080924.]: Logging df_agg: CpG#9
[20250402_080924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080924.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250402_080924.]: Entered 'hyperbolic_regression'-Function
[20250402_080924.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080925.]: Entered 'cubic_regression'-Function
[20250402_080925.]: 'cubic_regression': minmax = FALSE
[20250402_080926.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250402_080926.]: Logging df_agg: row_means
[20250402_080926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080926.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250402_080926.]: Entered 'hyperbolic_regression'-Function
[20250402_080926.]: 'hyperbolic_regression': minmax = FALSE
[20250402_080927.]: Entered 'cubic_regression'-Function
[20250402_080927.]: 'cubic_regression': minmax = FALSE
[20250402_080929.]: Entered 'solving_equations'-Function
[20250402_080929.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250402_080929.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250402_080929.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250402_080929.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250402_080929.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250402_080930.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250402_080930.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250402_080930.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250402_080930.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250402_080930.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250402_080930.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250402_080930.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080930.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250402_080930.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250402_080930.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250402_080930.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250402_080930.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250402_080930.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250402_080930.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250402_080930.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250402_080930.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250402_080930.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250402_080930.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250402_080930.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250402_080930.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250402_080930.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250402_080930.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250402_080930.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250402_080930.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250402_080930.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250402_080930.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250402_080930.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250402_080930.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250402_080930.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250402_080930.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250402_080930.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080930.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080931.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250402_080931.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250402_080931.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250402_080931.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250402_080931.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250402_080931.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250402_080931.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250402_080931.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250402_080931.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250402_080931.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250402_080931.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250402_080931.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250402_080931.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250402_080931.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250402_080931.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250402_080931.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250402_080931.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250402_080932.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250402_080932.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250402_080932.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250402_080932.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250402_080932.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250402_080932.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250402_080932.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080932.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250402_080932.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080932.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250402_080932.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250402_080932.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250402_080932.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250402_080932.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250402_080932.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250402_080932.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250402_080932.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250402_080932.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250402_080932.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250402_080932.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250402_080932.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250402_080932.]: Entered 'solving_equations'-Function
[20250402_080932.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250402_080932.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250402_080932.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250402_080932.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250402_080932.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250402_080932.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250402_080932.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250402_080932.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250402_080932.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250402_080932.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250402_080932.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_080932.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250402_080932.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250402_080932.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250402_080932.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250402_080933.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250402_080933.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250402_080933.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250402_080933.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250402_080933.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250402_080933.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250402_080933.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250402_080933.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250402_080933.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250402_080933.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250402_080933.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250402_080933.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250402_080933.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250402_080933.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250402_080933.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250402_080933.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250402_080933.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250402_080933.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250402_080933.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250402_080933.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250402_080933.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250402_080933.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250402_080933.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250402_080933.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250402_080933.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250402_080933.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250402_080933.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250402_080933.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250402_080933.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250402_080933.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_080933.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250402_080933.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080933.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_080934.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250402_080934.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250402_080934.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250402_080934.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250402_080934.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250402_080934.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250402_080934.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250402_080934.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250402_080934.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250402_080934.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250402_080934.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250402_080937.]: Entered 'clean_dt'-Function
[20250402_080937.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080937.]: got experimental data
[20250402_080937.]: Entered 'clean_dt'-Function
[20250402_080937.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_080937.]: got calibration data
[20250402_080937.]: ### Starting with regression calculations ###
[20250402_080937.]: Entered 'regression_type1'-Function
[20250402_080939.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080939.]: Logging df_agg: CpG#1
[20250402_080939.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080939.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_080939.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_080939.]: Entered 'hyperbolic_regression'-Function
[20250402_080939.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080941.]: Entered 'cubic_regression'-Function
[20250402_080941.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080942.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080942.]: Logging df_agg: CpG#2
[20250402_080942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080942.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_080942.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_080942.]: Entered 'hyperbolic_regression'-Function
[20250402_080942.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080943.]: Entered 'cubic_regression'-Function
[20250402_080943.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080944.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080944.]: Logging df_agg: CpG#3
[20250402_080944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080944.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_080944.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_080944.]: Entered 'hyperbolic_regression'-Function
[20250402_080944.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080945.]: Entered 'cubic_regression'-Function
[20250402_080945.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080946.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080946.]: Logging df_agg: CpG#4
[20250402_080946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080946.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_080946.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_080946.]: Entered 'hyperbolic_regression'-Function
[20250402_080946.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080948.]: Entered 'cubic_regression'-Function
[20250402_080948.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080949.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080949.]: Logging df_agg: CpG#5
[20250402_080949.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080949.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_080949.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_080949.]: Entered 'hyperbolic_regression'-Function
[20250402_080949.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080950.]: Entered 'cubic_regression'-Function
[20250402_080950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080943.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080944.]: Logging df_agg: CpG#6
[20250402_080944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080944.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_080944.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_080944.]: Entered 'hyperbolic_regression'-Function
[20250402_080944.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080944.]: Entered 'cubic_regression'-Function
[20250402_080944.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080945.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080945.]: Logging df_agg: CpG#7
[20250402_080945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080945.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_080945.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_080945.]: Entered 'hyperbolic_regression'-Function
[20250402_080945.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080947.]: Entered 'cubic_regression'-Function
[20250402_080947.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080947.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080947.]: Logging df_agg: CpG#8
[20250402_080948.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080948.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_080948.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_080948.]: Entered 'hyperbolic_regression'-Function
[20250402_080948.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080949.]: Entered 'cubic_regression'-Function
[20250402_080949.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080951.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080951.]: Logging df_agg: CpG#9
[20250402_080951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080951.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_080951.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_080951.]: Entered 'hyperbolic_regression'-Function
[20250402_080951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080953.]: Entered 'cubic_regression'-Function
[20250402_080953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080953.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080953.]: Logging df_agg: row_means
[20250402_080953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_080953.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_080953.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_080953.]: Entered 'hyperbolic_regression'-Function
[20250402_080953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_080956.]: Entered 'cubic_regression'-Function
[20250402_080956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081005.]: Entered 'regression_type1'-Function
[20250402_081007.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081008.]: Logging df_agg: CpG#1
[20250402_081008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081008.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_081008.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081008.]: Entered 'hyperbolic_regression'-Function
[20250402_081008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081010.]: Entered 'cubic_regression'-Function
[20250402_081010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081011.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081011.]: Logging df_agg: CpG#2
[20250402_081011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081011.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_081011.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081011.]: Entered 'hyperbolic_regression'-Function
[20250402_081011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081012.]: Entered 'cubic_regression'-Function
[20250402_081012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081013.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081013.]: Logging df_agg: CpG#3
[20250402_081013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081013.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_081013.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081013.]: Entered 'hyperbolic_regression'-Function
[20250402_081013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081015.]: Entered 'cubic_regression'-Function
[20250402_081015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081016.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081016.]: Logging df_agg: CpG#4
[20250402_081016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081016.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_081016.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081016.]: Entered 'hyperbolic_regression'-Function
[20250402_081016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081018.]: Entered 'cubic_regression'-Function
[20250402_081018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081019.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081019.]: Logging df_agg: CpG#5
[20250402_081019.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081019.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_081019.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081019.]: Entered 'hyperbolic_regression'-Function
[20250402_081019.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081020.]: Entered 'cubic_regression'-Function
[20250402_081020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081008.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081009.]: Logging df_agg: CpG#6
[20250402_081009.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081009.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_081009.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081009.]: Entered 'hyperbolic_regression'-Function
[20250402_081009.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081010.]: Entered 'cubic_regression'-Function
[20250402_081010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081011.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081011.]: Logging df_agg: CpG#7
[20250402_081011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081011.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_081011.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081011.]: Entered 'hyperbolic_regression'-Function
[20250402_081011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081012.]: Entered 'cubic_regression'-Function
[20250402_081012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081013.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081013.]: Logging df_agg: CpG#8
[20250402_081013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081013.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_081013.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081013.]: Entered 'hyperbolic_regression'-Function
[20250402_081013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081014.]: Entered 'cubic_regression'-Function
[20250402_081014.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081015.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081015.]: Logging df_agg: CpG#9
[20250402_081015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081015.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_081015.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081015.]: Entered 'hyperbolic_regression'-Function
[20250402_081015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081016.]: Entered 'cubic_regression'-Function
[20250402_081016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081017.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081017.]: Logging df_agg: row_means
[20250402_081017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081017.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_081017.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081017.]: Entered 'hyperbolic_regression'-Function
[20250402_081017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081018.]: Entered 'cubic_regression'-Function
[20250402_081018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081028.]: Entered 'clean_dt'-Function
[20250402_081028.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_081028.]: got experimental data
[20250402_081028.]: Entered 'clean_dt'-Function
[20250402_081028.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_081028.]: got calibration data
[20250402_081028.]: ### Starting with regression calculations ###
[20250402_081028.]: Entered 'regression_type1'-Function
[20250402_081030.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081031.]: Logging df_agg: CpG#1
[20250402_081031.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081031.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_081031.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081031.]: Entered 'hyperbolic_regression'-Function
[20250402_081031.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081032.]: Entered 'cubic_regression'-Function
[20250402_081032.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081033.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081033.]: Logging df_agg: CpG#2
[20250402_081033.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081033.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_081033.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081033.]: Entered 'hyperbolic_regression'-Function
[20250402_081033.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081034.]: Entered 'cubic_regression'-Function
[20250402_081034.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081036.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081036.]: Logging df_agg: CpG#3
[20250402_081036.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081036.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_081036.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081036.]: Entered 'hyperbolic_regression'-Function
[20250402_081036.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081037.]: Entered 'cubic_regression'-Function
[20250402_081037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081038.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081038.]: Logging df_agg: CpG#4
[20250402_081038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081038.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_081038.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081038.]: Entered 'hyperbolic_regression'-Function
[20250402_081038.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081040.]: Entered 'cubic_regression'-Function
[20250402_081040.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081040.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081040.]: Logging df_agg: CpG#5
[20250402_081040.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081040.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_081040.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081041.]: Entered 'hyperbolic_regression'-Function
[20250402_081041.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081042.]: Entered 'cubic_regression'-Function
[20250402_081042.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081033.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081033.]: Logging df_agg: CpG#6
[20250402_081033.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081033.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_081033.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081033.]: Entered 'hyperbolic_regression'-Function
[20250402_081033.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081035.]: Entered 'cubic_regression'-Function
[20250402_081035.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081035.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081035.]: Logging df_agg: CpG#7
[20250402_081035.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081035.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_081035.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081035.]: Entered 'hyperbolic_regression'-Function
[20250402_081035.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081037.]: Entered 'cubic_regression'-Function
[20250402_081037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081038.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081038.]: Logging df_agg: CpG#8
[20250402_081038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081038.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_081038.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081038.]: Entered 'hyperbolic_regression'-Function
[20250402_081038.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081040.]: Entered 'cubic_regression'-Function
[20250402_081040.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081041.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081041.]: Logging df_agg: CpG#9
[20250402_081041.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081041.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_081041.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081041.]: Entered 'hyperbolic_regression'-Function
[20250402_081041.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081042.]: Entered 'cubic_regression'-Function
[20250402_081042.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081043.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081043.]: Logging df_agg: row_means
[20250402_081043.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081043.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_081043.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081043.]: Entered 'hyperbolic_regression'-Function
[20250402_081043.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081044.]: Entered 'cubic_regression'-Function
[20250402_081044.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081054.]: Entered 'regression_type1'-Function
[20250402_081056.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081056.]: Logging df_agg: CpG#1
[20250402_081056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081056.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_081056.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_081056.]: Entered 'hyperbolic_regression'-Function
[20250402_081056.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081058.]: Entered 'cubic_regression'-Function
[20250402_081058.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081058.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081058.]: Logging df_agg: CpG#2
[20250402_081058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081058.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_081058.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_081058.]: Entered 'hyperbolic_regression'-Function
[20250402_081058.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081100.]: Entered 'cubic_regression'-Function
[20250402_081100.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081101.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081101.]: Logging df_agg: CpG#3
[20250402_081101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081101.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_081101.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_081101.]: Entered 'hyperbolic_regression'-Function
[20250402_081101.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081102.]: Entered 'cubic_regression'-Function
[20250402_081102.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081103.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081103.]: Logging df_agg: CpG#4
[20250402_081103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081103.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_081103.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_081103.]: Entered 'hyperbolic_regression'-Function
[20250402_081103.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081104.]: Entered 'cubic_regression'-Function
[20250402_081104.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081105.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081105.]: Logging df_agg: CpG#5
[20250402_081105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081105.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_081105.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_081105.]: Entered 'hyperbolic_regression'-Function
[20250402_081105.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081106.]: Entered 'cubic_regression'-Function
[20250402_081106.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081057.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081057.]: Logging df_agg: CpG#6
[20250402_081057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081057.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_081057.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_081057.]: Entered 'hyperbolic_regression'-Function
[20250402_081057.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081058.]: Entered 'cubic_regression'-Function
[20250402_081058.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081059.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081059.]: Logging df_agg: CpG#7
[20250402_081059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081059.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_081059.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_081059.]: Entered 'hyperbolic_regression'-Function
[20250402_081059.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081059.]: Entered 'cubic_regression'-Function
[20250402_081059.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081100.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081100.]: Logging df_agg: CpG#8
[20250402_081100.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081100.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_081100.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_081100.]: Entered 'hyperbolic_regression'-Function
[20250402_081100.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081101.]: Entered 'cubic_regression'-Function
[20250402_081101.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081102.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081102.]: Logging df_agg: CpG#9
[20250402_081102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081102.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_081102.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_081102.]: Entered 'hyperbolic_regression'-Function
[20250402_081102.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081103.]: Entered 'cubic_regression'-Function
[20250402_081103.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081103.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081103.]: Logging df_agg: row_means
[20250402_081103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081103.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_081103.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_081103.]: Entered 'hyperbolic_regression'-Function
[20250402_081103.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081104.]: Entered 'cubic_regression'-Function
[20250402_081104.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081109.]: Entered 'solving_equations'-Function
[20250402_081109.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250402_081109.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250402_081109.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250402_081109.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250402_081109.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250402_081109.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250402_081109.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250402_081109.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250402_081109.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081109.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081109.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250402_081109.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250402_081109.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250402_081109.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250402_081109.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250402_081109.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250402_081109.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250402_081109.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250402_081109.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081109.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081109.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250402_081109.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250402_081109.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250402_081109.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250402_081109.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250402_081109.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250402_081109.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250402_081109.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250402_081109.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081109.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081109.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250402_081109.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250402_081109.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250402_081109.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250402_081109.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250402_081109.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250402_081110.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250402_081110.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250402_081110.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081110.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081110.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250402_081110.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250402_081110.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250402_081110.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250402_081110.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250402_081110.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250402_081110.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250402_081110.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250402_081110.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081110.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081110.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250402_081110.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250402_081110.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250402_081110.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250402_081110.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250402_081110.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250402_081110.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250402_081110.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250402_081110.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081110.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081110.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250402_081110.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250402_081110.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250402_081110.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250402_081110.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250402_081110.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250402_081110.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250402_081110.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250402_081110.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081110.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081110.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250402_081110.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250402_081110.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250402_081110.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250402_081110.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250402_081110.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250402_081110.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250402_081110.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250402_081110.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081110.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081111.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250402_081111.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250402_081111.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250402_081111.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250402_081111.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250402_081111.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250402_081111.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250402_081111.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250402_081111.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081111.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081111.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250402_081111.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250402_081111.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250402_081111.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250402_081111.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250402_081111.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250402_081111.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250402_081111.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250402_081111.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081111.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081111.]: ### Starting with regression calculations ###
[20250402_081111.]: Entered 'regression_type1'-Function
[20250402_081113.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250402_081114.]: Logging df_agg: CpG#1
[20250402_081114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081114.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250402_081114.]: Entered 'hyperbolic_regression'-Function
[20250402_081114.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081116.]: Entered 'cubic_regression'-Function
[20250402_081116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081117.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250402_081117.]: Logging df_agg: CpG#2
[20250402_081117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081117.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250402_081117.]: Entered 'hyperbolic_regression'-Function
[20250402_081117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081118.]: Entered 'cubic_regression'-Function
[20250402_081118.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081119.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250402_081119.]: Logging df_agg: CpG#3
[20250402_081119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081119.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250402_081119.]: Entered 'hyperbolic_regression'-Function
[20250402_081119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081120.]: Entered 'cubic_regression'-Function
[20250402_081120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081121.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250402_081121.]: Logging df_agg: CpG#4
[20250402_081121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081121.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250402_081121.]: Entered 'hyperbolic_regression'-Function
[20250402_081121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081123.]: Entered 'cubic_regression'-Function
[20250402_081123.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081124.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250402_081124.]: Logging df_agg: CpG#5
[20250402_081124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081124.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250402_081124.]: Entered 'hyperbolic_regression'-Function
[20250402_081124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081125.]: Entered 'cubic_regression'-Function
[20250402_081125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081115.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250402_081115.]: Logging df_agg: CpG#6
[20250402_081115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081115.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250402_081115.]: Entered 'hyperbolic_regression'-Function
[20250402_081115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081116.]: Entered 'cubic_regression'-Function
[20250402_081116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081117.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250402_081117.]: Logging df_agg: CpG#7
[20250402_081117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081117.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250402_081117.]: Entered 'hyperbolic_regression'-Function
[20250402_081117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081118.]: Entered 'cubic_regression'-Function
[20250402_081118.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081119.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250402_081119.]: Logging df_agg: CpG#8
[20250402_081119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081119.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250402_081119.]: Entered 'hyperbolic_regression'-Function
[20250402_081120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081121.]: Entered 'cubic_regression'-Function
[20250402_081121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081122.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250402_081122.]: Logging df_agg: CpG#9
[20250402_081122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081122.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250402_081122.]: Entered 'hyperbolic_regression'-Function
[20250402_081122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081124.]: Entered 'cubic_regression'-Function
[20250402_081124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081124.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250402_081124.]: Logging df_agg: row_means
[20250402_081124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081124.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250402_081124.]: Entered 'hyperbolic_regression'-Function
[20250402_081124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081125.]: Entered 'cubic_regression'-Function
[20250402_081125.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081129.]: Entered 'solving_equations'-Function
[20250402_081129.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_081129.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081129.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081129.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081129.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_081129.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081129.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_081129.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081129.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081129.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081129.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081129.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081129.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081129.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081130.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081130.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081130.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081130.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_081130.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_081130.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081130.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081130.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081131.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081131.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_081131.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081131.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081131.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_081131.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081131.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_081132.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081132.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_081132.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081132.]: ### Starting with regression calculations ###
[20250402_081132.]: Entered 'regression_type1'-Function
[20250402_081134.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250402_081135.]: Logging df_agg: CpG#1
[20250402_081135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081135.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250402_081135.]: Entered 'hyperbolic_regression'-Function
[20250402_081135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081136.]: Entered 'cubic_regression'-Function
[20250402_081136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081137.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250402_081137.]: Logging df_agg: CpG#2
[20250402_081137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081137.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250402_081137.]: Entered 'hyperbolic_regression'-Function
[20250402_081137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081138.]: Entered 'cubic_regression'-Function
[20250402_081138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081139.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250402_081139.]: Logging df_agg: CpG#3
[20250402_081139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081139.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250402_081139.]: Entered 'hyperbolic_regression'-Function
[20250402_081139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081140.]: Entered 'cubic_regression'-Function
[20250402_081140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081141.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250402_081141.]: Logging df_agg: CpG#4
[20250402_081141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081141.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250402_081141.]: Entered 'hyperbolic_regression'-Function
[20250402_081141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081142.]: Entered 'cubic_regression'-Function
[20250402_081142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081143.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250402_081143.]: Logging df_agg: CpG#5
[20250402_081143.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081143.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250402_081143.]: Entered 'hyperbolic_regression'-Function
[20250402_081143.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081145.]: Entered 'cubic_regression'-Function
[20250402_081145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081135.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250402_081136.]: Logging df_agg: CpG#6
[20250402_081136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081136.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250402_081136.]: Entered 'hyperbolic_regression'-Function
[20250402_081136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081137.]: Entered 'cubic_regression'-Function
[20250402_081137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081138.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250402_081138.]: Logging df_agg: CpG#7
[20250402_081138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081138.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250402_081138.]: Entered 'hyperbolic_regression'-Function
[20250402_081138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081140.]: Entered 'cubic_regression'-Function
[20250402_081140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081141.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250402_081141.]: Logging df_agg: CpG#8
[20250402_081141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081141.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250402_081141.]: Entered 'hyperbolic_regression'-Function
[20250402_081141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081141.]: Entered 'cubic_regression'-Function
[20250402_081141.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081142.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250402_081142.]: Logging df_agg: CpG#9
[20250402_081142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081142.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250402_081142.]: Entered 'hyperbolic_regression'-Function
[20250402_081142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081143.]: Entered 'cubic_regression'-Function
[20250402_081143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081144.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250402_081144.]: Logging df_agg: row_means
[20250402_081144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_081144.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250402_081144.]: Entered 'hyperbolic_regression'-Function
[20250402_081144.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081145.]: Entered 'cubic_regression'-Function
[20250402_081145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_081148.]: Entered 'solving_equations'-Function
[20250402_081148.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250402_081148.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250402_081148.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250402_081148.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250402_081148.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250402_081148.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250402_081148.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250402_081148.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250402_081148.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250402_081148.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250402_081148.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250402_081148.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081148.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250402_081148.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250402_081148.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250402_081148.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250402_081148.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250402_081148.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250402_081148.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250402_081148.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250402_081148.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250402_081148.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250402_081148.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250402_081148.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250402_081148.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250402_081148.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250402_081148.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250402_081148.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250402_081148.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250402_081148.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250402_081148.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250402_081148.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250402_081148.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250402_081148.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250402_081148.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250402_081148.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081148.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250402_081148.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250402_081148.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250402_081148.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250402_081148.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250402_081148.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250402_081148.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250402_081148.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250402_081148.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250402_081148.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250402_081148.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250402_081148.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250402_081148.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250402_081148.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250402_081148.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250402_081148.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250402_081148.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250402_081148.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250402_081148.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250402_081148.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250402_081148.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250402_081148.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250402_081148.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250402_081148.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081149.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250402_081149.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250402_081149.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250402_081149.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250402_081149.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250402_081149.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250402_081149.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250402_081149.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250402_081149.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250402_081149.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250402_081149.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250402_081149.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250402_081149.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250402_081149.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250402_081149.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250402_081149.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250402_081149.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250402_081149.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250402_081149.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250402_081149.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250402_081149.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250402_081149.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250402_081149.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250402_081149.]: Entered 'solving_equations'-Function
[20250402_081149.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250402_081149.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250402_081149.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250402_081149.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250402_081149.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250402_081149.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250402_081149.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250402_081149.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250402_081149.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081149.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081149.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081149.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_081150.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081150.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250402_081150.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250402_081150.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250402_081150.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250402_081150.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250402_081150.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250402_081150.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250402_081150.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250402_081150.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081150.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081150.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250402_081150.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250402_081150.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250402_081150.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250402_081150.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250402_081150.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250402_081150.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250402_081150.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250402_081150.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081150.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081150.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_081150.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_081150.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250402_081150.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250402_081150.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250402_081150.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250402_081151.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250402_081151.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250402_081151.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250402_081151.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250402_081151.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081151.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081151.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250402_081151.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250402_081151.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250402_081151.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250402_081151.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250402_081151.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250402_081151.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250402_081151.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250402_081151.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081151.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081151.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_081151.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_081151.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250402_081151.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250402_081151.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250402_081151.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250402_081151.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250402_081151.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250402_081151.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250402_081151.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250402_081151.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081151.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_081151.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250402_081151.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250402_081151.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250402_081151.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250402_081151.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250402_081151.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250402_081151.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250402_081151.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250402_081151.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_081151.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
[20250402_081538.]: Entered 'clean_dt'-Function
[20250402_081538.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_081538.]: got experimental data
[20250402_081538.]: Entered 'clean_dt'-Function
[20250402_081538.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250402_081538.]: got experimental data
[20250402_081540.]: Entered 'clean_dt'-Function
[20250402_081540.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_081540.]: got calibration data
[20250402_081540.]: Entered 'clean_dt'-Function
[20250402_081540.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_081540.]: got calibration data
[20250402_081540.]: Entered 'hyperbolic_regression'-Function
[20250402_081540.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.3.4
Check: tests
Result: ERROR
Running ‘testthat.R’ [4m/11m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(rBiasCorrection)
>
> local_edition(3)
>
> test_check("rBiasCorrection")
[20250402_082735.]: Entered 'clean_dt'-Function
[20250402_082735.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082735.]: got experimental data
[20250402_082735.]: Entered 'clean_dt'-Function
[20250402_082735.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082735.]: got calibration data
[20250402_082735.]: ### Starting with regression calculations ###
[20250402_082735.]: Entered 'regression_type1'-Function
[20250402_082736.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082736.]: Logging df_agg: CpG#1
[20250402_082736.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082736.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082736.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082736.]: Entered 'hyperbolic_regression'-Function
[20250402_082736.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082738.]: Entered 'cubic_regression'-Function
[20250402_082738.]: 'cubic_regression': minmax = FALSE
[20250402_082738.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082738.]: Logging df_agg: CpG#2
[20250402_082738.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082738.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082738.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082738.]: Entered 'hyperbolic_regression'-Function
[20250402_082738.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082739.]: Entered 'cubic_regression'-Function
[20250402_082739.]: 'cubic_regression': minmax = FALSE
[20250402_082739.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082739.]: Logging df_agg: CpG#3
[20250402_082739.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082739.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082739.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082739.]: Entered 'hyperbolic_regression'-Function
[20250402_082739.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082740.]: Entered 'cubic_regression'-Function
[20250402_082740.]: 'cubic_regression': minmax = FALSE
[20250402_082740.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082740.]: Logging df_agg: CpG#4
[20250402_082740.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082740.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082740.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082740.]: Entered 'hyperbolic_regression'-Function
[20250402_082740.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082741.]: Entered 'cubic_regression'-Function
[20250402_082741.]: 'cubic_regression': minmax = FALSE
[20250402_082741.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082741.]: Logging df_agg: CpG#5
[20250402_082741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082741.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_082741.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082741.]: Entered 'hyperbolic_regression'-Function
[20250402_082741.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082742.]: Entered 'cubic_regression'-Function
[20250402_082742.]: 'cubic_regression': minmax = FALSE
[20250402_082738.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082738.]: Logging df_agg: CpG#6
[20250402_082738.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082738.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082738.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082738.]: Entered 'hyperbolic_regression'-Function
[20250402_082738.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082739.]: Entered 'cubic_regression'-Function
[20250402_082739.]: 'cubic_regression': minmax = FALSE
[20250402_082739.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082739.]: Logging df_agg: CpG#7
[20250402_082739.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082739.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082739.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082739.]: Entered 'hyperbolic_regression'-Function
[20250402_082739.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082740.]: Entered 'cubic_regression'-Function
[20250402_082740.]: 'cubic_regression': minmax = FALSE
[20250402_082740.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082740.]: Logging df_agg: CpG#8
[20250402_082740.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082740.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082740.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082740.]: Entered 'hyperbolic_regression'-Function
[20250402_082740.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082741.]: Entered 'cubic_regression'-Function
[20250402_082741.]: 'cubic_regression': minmax = FALSE
[20250402_082741.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082741.]: Logging df_agg: CpG#9
[20250402_082741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082741.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082741.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082741.]: Entered 'hyperbolic_regression'-Function
[20250402_082741.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082741.]: Entered 'cubic_regression'-Function
[20250402_082741.]: 'cubic_regression': minmax = FALSE
[20250402_082741.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082741.]: Logging df_agg: row_means
[20250402_082741.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082741.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082741.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082741.]: Entered 'hyperbolic_regression'-Function
[20250402_082741.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082742.]: Entered 'cubic_regression'-Function
[20250402_082742.]: 'cubic_regression': minmax = FALSE
[20250402_082754.]: Entered 'regression_type1'-Function
[20250402_082756.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082756.]: Logging df_agg: CpG#1
[20250402_082756.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082756.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082756.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082756.]: Entered 'hyperbolic_regression'-Function
[20250402_082756.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082758.]: Entered 'cubic_regression'-Function
[20250402_082758.]: 'cubic_regression': minmax = FALSE
[20250402_082758.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082758.]: Logging df_agg: CpG#2
[20250402_082758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082758.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082758.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082758.]: Entered 'hyperbolic_regression'-Function
[20250402_082758.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082759.]: Entered 'cubic_regression'-Function
[20250402_082759.]: 'cubic_regression': minmax = FALSE
[20250402_082759.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082759.]: Logging df_agg: CpG#3
[20250402_082759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082759.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082759.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082759.]: Entered 'hyperbolic_regression'-Function
[20250402_082759.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082801.]: Entered 'cubic_regression'-Function
[20250402_082801.]: 'cubic_regression': minmax = FALSE
[20250402_082801.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082801.]: Logging df_agg: CpG#4
[20250402_082801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082801.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082801.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082801.]: Entered 'hyperbolic_regression'-Function
[20250402_082801.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082803.]: Entered 'cubic_regression'-Function
[20250402_082803.]: 'cubic_regression': minmax = FALSE
[20250402_082803.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082803.]: Logging df_agg: CpG#5
[20250402_082803.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082803.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_082803.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082803.]: Entered 'hyperbolic_regression'-Function
[20250402_082803.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082804.]: Entered 'cubic_regression'-Function
[20250402_082804.]: 'cubic_regression': minmax = FALSE
[20250402_082757.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082757.]: Logging df_agg: CpG#6
[20250402_082757.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082757.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082757.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082757.]: Entered 'hyperbolic_regression'-Function
[20250402_082757.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082758.]: Entered 'cubic_regression'-Function
[20250402_082758.]: 'cubic_regression': minmax = FALSE
[20250402_082758.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082758.]: Logging df_agg: CpG#7
[20250402_082758.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082758.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082758.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082758.]: Entered 'hyperbolic_regression'-Function
[20250402_082758.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082759.]: Entered 'cubic_regression'-Function
[20250402_082759.]: 'cubic_regression': minmax = FALSE
[20250402_082800.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082800.]: Logging df_agg: CpG#8
[20250402_082800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082800.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082800.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082800.]: Entered 'hyperbolic_regression'-Function
[20250402_082800.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082801.]: Entered 'cubic_regression'-Function
[20250402_082801.]: 'cubic_regression': minmax = FALSE
[20250402_082801.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082801.]: Logging df_agg: CpG#9
[20250402_082801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082801.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082801.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082801.]: Entered 'hyperbolic_regression'-Function
[20250402_082801.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082802.]: Entered 'cubic_regression'-Function
[20250402_082802.]: 'cubic_regression': minmax = FALSE
[20250402_082802.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082802.]: Logging df_agg: row_means
[20250402_082802.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082802.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082802.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082802.]: Entered 'hyperbolic_regression'-Function
[20250402_082802.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082803.]: Entered 'cubic_regression'-Function
[20250402_082803.]: 'cubic_regression': minmax = FALSE
[20250402_082810.]: Entered 'clean_dt'-Function
[20250402_082810.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082810.]: got experimental data
[20250402_082810.]: Entered 'clean_dt'-Function
[20250402_082810.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082810.]: got calibration data
[20250402_082810.]: ### Starting with regression calculations ###
[20250402_082810.]: Entered 'regression_type1'-Function
[20250402_082812.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082812.]: Logging df_agg: CpG#1
[20250402_082812.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082812.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082812.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082812.]: Entered 'hyperbolic_regression'-Function
[20250402_082812.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082813.]: Entered 'cubic_regression'-Function
[20250402_082813.]: 'cubic_regression': minmax = FALSE
[20250402_082813.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082814.]: Logging df_agg: CpG#2
[20250402_082814.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082814.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082814.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082814.]: Entered 'hyperbolic_regression'-Function
[20250402_082814.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082815.]: Entered 'cubic_regression'-Function
[20250402_082815.]: 'cubic_regression': minmax = FALSE
[20250402_082815.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082815.]: Logging df_agg: CpG#3
[20250402_082815.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082815.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082815.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082815.]: Entered 'hyperbolic_regression'-Function
[20250402_082815.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082817.]: Entered 'cubic_regression'-Function
[20250402_082817.]: 'cubic_regression': minmax = FALSE
[20250402_082817.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082817.]: Logging df_agg: CpG#4
[20250402_082817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082817.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082817.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082817.]: Entered 'hyperbolic_regression'-Function
[20250402_082817.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082819.]: Entered 'cubic_regression'-Function
[20250402_082819.]: 'cubic_regression': minmax = FALSE
[20250402_082819.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082819.]: Logging df_agg: CpG#5
[20250402_082819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082819.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_082819.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082819.]: Entered 'hyperbolic_regression'-Function
[20250402_082819.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082820.]: Entered 'cubic_regression'-Function
[20250402_082820.]: 'cubic_regression': minmax = FALSE
[20250402_082813.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082814.]: Logging df_agg: CpG#6
[20250402_082814.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082814.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082814.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082814.]: Entered 'hyperbolic_regression'-Function
[20250402_082814.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082815.]: Entered 'cubic_regression'-Function
[20250402_082815.]: 'cubic_regression': minmax = FALSE
[20250402_082815.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082815.]: Logging df_agg: CpG#7
[20250402_082815.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082815.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082815.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082815.]: Entered 'hyperbolic_regression'-Function
[20250402_082815.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082817.]: Entered 'cubic_regression'-Function
[20250402_082817.]: 'cubic_regression': minmax = FALSE
[20250402_082817.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082817.]: Logging df_agg: CpG#8
[20250402_082817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082817.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082817.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082817.]: Entered 'hyperbolic_regression'-Function
[20250402_082817.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082818.]: Entered 'cubic_regression'-Function
[20250402_082818.]: 'cubic_regression': minmax = FALSE
[20250402_082818.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082818.]: Logging df_agg: CpG#9
[20250402_082818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082818.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082818.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082818.]: Entered 'hyperbolic_regression'-Function
[20250402_082818.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082819.]: Entered 'cubic_regression'-Function
[20250402_082820.]: 'cubic_regression': minmax = FALSE
[20250402_082820.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082820.]: Logging df_agg: row_means
[20250402_082820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082820.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082820.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082820.]: Entered 'hyperbolic_regression'-Function
[20250402_082820.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082820.]: Entered 'cubic_regression'-Function
[20250402_082820.]: 'cubic_regression': minmax = FALSE
[20250402_082828.]: Entered 'regression_type1'-Function
[20250402_082830.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082831.]: Logging df_agg: CpG#1
[20250402_082831.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082831.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082831.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082831.]: Entered 'hyperbolic_regression'-Function
[20250402_082831.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082833.]: Entered 'cubic_regression'-Function
[20250402_082833.]: 'cubic_regression': minmax = FALSE
[20250402_082833.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082833.]: Logging df_agg: CpG#2
[20250402_082833.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082833.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082833.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082833.]: Entered 'hyperbolic_regression'-Function
[20250402_082833.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082834.]: Entered 'cubic_regression'-Function
[20250402_082834.]: 'cubic_regression': minmax = FALSE
[20250402_082834.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082834.]: Logging df_agg: CpG#3
[20250402_082834.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082834.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082834.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082834.]: Entered 'hyperbolic_regression'-Function
[20250402_082834.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082835.]: Entered 'cubic_regression'-Function
[20250402_082836.]: 'cubic_regression': minmax = FALSE
[20250402_082836.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082836.]: Logging df_agg: CpG#4
[20250402_082836.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082836.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082836.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082836.]: Entered 'hyperbolic_regression'-Function
[20250402_082836.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082837.]: Entered 'cubic_regression'-Function
[20250402_082837.]: 'cubic_regression': minmax = FALSE
[20250402_082837.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082837.]: Logging df_agg: CpG#5
[20250402_082837.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082837.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_082837.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082837.]: Entered 'hyperbolic_regression'-Function
[20250402_082837.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082839.]: Entered 'cubic_regression'-Function
[20250402_082839.]: 'cubic_regression': minmax = FALSE
[20250402_082831.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082832.]: Logging df_agg: CpG#6
[20250402_082832.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082832.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082832.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082832.]: Entered 'hyperbolic_regression'-Function
[20250402_082832.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082833.]: Entered 'cubic_regression'-Function
[20250402_082833.]: 'cubic_regression': minmax = FALSE
[20250402_082833.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082833.]: Logging df_agg: CpG#7
[20250402_082833.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082833.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082833.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082833.]: Entered 'hyperbolic_regression'-Function
[20250402_082833.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082835.]: Entered 'cubic_regression'-Function
[20250402_082835.]: 'cubic_regression': minmax = FALSE
[20250402_082835.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082835.]: Logging df_agg: CpG#8
[20250402_082835.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082835.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082835.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082835.]: Entered 'hyperbolic_regression'-Function
[20250402_082835.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082836.]: Entered 'cubic_regression'-Function
[20250402_082836.]: 'cubic_regression': minmax = FALSE
[20250402_082836.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082836.]: Logging df_agg: CpG#9
[20250402_082836.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082836.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082836.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082836.]: Entered 'hyperbolic_regression'-Function
[20250402_082836.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082837.]: Entered 'cubic_regression'-Function
[20250402_082837.]: 'cubic_regression': minmax = FALSE
[20250402_082837.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082837.]: Logging df_agg: row_means
[20250402_082837.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082837.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082837.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082837.]: Entered 'hyperbolic_regression'-Function
[20250402_082837.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082838.]: Entered 'cubic_regression'-Function
[20250402_082838.]: 'cubic_regression': minmax = FALSE
[20250402_082843.]: Entered 'solving_equations'-Function
[20250402_082843.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250402_082843.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250402_082843.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250402_082843.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250402_082843.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250402_082843.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250402_082843.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250402_082843.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250402_082843.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250402_082843.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250402_082843.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 1.13660501904968
[20250402_082843.]: Samplename: 0
Root: 1.137
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.4129696733689
[20250402_082843.]: Samplename: 12.5
Root: 11.413
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.174000526428
[20250402_082843.]: Samplename: 25
Root: 26.174
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.1050449117028
[20250402_082843.]: Samplename: 37.5
Root: 35.105
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.685500330611
[20250402_082843.]: Samplename: 50
Root: 47.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.1440494417104
[20250402_082843.]: Samplename: 62.5
Root: 67.144
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7644668894086
[20250402_082843.]: Samplename: 75
Root: 75.764
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.4054158616395
[20250402_082843.]: Samplename: 87.5
Root: 84.405
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.94827248399
[20250402_082843.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.948
--> '100 < root < 110' --> substitute 100
[20250402_082843.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250402_082843.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250402_082843.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250402_082843.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250402_082843.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250402_082843.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250402_082843.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250402_082843.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250402_082843.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250402_082843.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250402_082843.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250402_082843.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250402_082843.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250402_082843.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250402_082843.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250402_082843.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250402_082843.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250402_082843.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250402_082843.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250402_082843.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250402_082843.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 2.41558626275183
[20250402_082843.]: Samplename: 0
Root: 2.416
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.1649674907454
[20250402_082843.]: Samplename: 12.5
Root: 10.165
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.9830820412762
[20250402_082843.]: Samplename: 25
Root: 23.983
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2773619900429
[20250402_082843.]: Samplename: 37.5
Root: 37.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.8659386543864
[20250402_082843.]: Samplename: 50
Root: 50.866
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.4342273571069
[20250402_082843.]: Samplename: 62.5
Root: 62.434
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.3915260534323
[20250402_082843.]: Samplename: 75
Root: 76.392
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.159788778566
[20250402_082843.]: Samplename: 87.5
Root: 86.16
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.267759893323
[20250402_082843.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.268
--> '100 < root < 110' --> substitute 100
[20250402_082844.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250402_082844.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250402_082844.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250402_082844.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250402_082844.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250402_082844.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250402_082844.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250402_082844.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250402_082844.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250402_082844.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250402_082844.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250402_082844.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250402_082844.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250402_082844.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250402_082844.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250402_082844.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250402_082844.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250402_082844.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250402_082844.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250402_082844.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250402_082844.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 2.80068218205093
[20250402_082844.]: Samplename: 0
Root: 2.801
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.27535134596596
[20250402_082844.]: Samplename: 12.5
Root: 9.275
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4762621928197
[20250402_082844.]: Samplename: 25
Root: 25.476
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0122075735416
[20250402_082844.]: Samplename: 37.5
Root: 34.012
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.7842655662325
[20250402_082844.]: Samplename: 50
Root: 51.784
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.6732311906145
[20250402_082844.]: Samplename: 62.5
Root: 64.673
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.4326978859189
[20250402_082844.]: Samplename: 75
Root: 78.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.3427232852719
[20250402_082844.]: Samplename: 87.5
Root: 81.343
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.964406640583
[20250402_082844.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.964
--> '100 < root < 110' --> substitute 100
[20250402_082844.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: -2.13403721845678
[20250402_082844.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.134
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.5082192457956
[20250402_082844.]: Samplename: 12.5
Root: 10.508
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9164567253388
[20250402_082844.]: Samplename: 25
Root: 26.916
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.8334779159501
[20250402_082844.]: Samplename: 37.5
Root: 36.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.0097895977263
[20250402_082844.]: Samplename: 50
Root: 52.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8930527921581
[20250402_082844.]: Samplename: 62.5
Root: 64.893
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.5671055499357
[20250402_082844.]: Samplename: 75
Root: 74.567
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.5294954832669
[20250402_082844.]: Samplename: 87.5
Root: 84.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.047146466811
[20250402_082844.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.047
--> '100 < root < 110' --> substitute 100
[20250402_082844.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250402_082845.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250402_082845.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250402_082845.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250402_082845.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250402_082845.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250402_082845.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250402_082845.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250402_082845.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250402_082845.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250402_082845.]: ### Starting with regression calculations ###
[20250402_082845.]: Entered 'regression_type1'-Function
[20250402_082848.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250402_082848.]: Logging df_agg: CpG#1
[20250402_082848.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082848.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)
[20250402_082848.]: Entered 'hyperbolic_regression'-Function
[20250402_082848.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082849.]: Entered 'cubic_regression'-Function
[20250402_082849.]: 'cubic_regression': minmax = FALSE
[20250402_082850.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250402_082850.]: Logging df_agg: CpG#2
[20250402_082850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082850.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)
[20250402_082850.]: Entered 'hyperbolic_regression'-Function
[20250402_082850.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082851.]: Entered 'cubic_regression'-Function
[20250402_082851.]: 'cubic_regression': minmax = FALSE
[20250402_082851.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250402_082851.]: Logging df_agg: CpG#3
[20250402_082851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082851.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)
[20250402_082851.]: Entered 'hyperbolic_regression'-Function
[20250402_082851.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082852.]: Entered 'cubic_regression'-Function
[20250402_082852.]: 'cubic_regression': minmax = FALSE
[20250402_082852.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250402_082852.]: Logging df_agg: CpG#4
[20250402_082852.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082852.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)
[20250402_082852.]: Entered 'hyperbolic_regression'-Function
[20250402_082852.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082853.]: Entered 'cubic_regression'-Function
[20250402_082853.]: 'cubic_regression': minmax = FALSE
[20250402_082853.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250402_082853.]: Logging df_agg: CpG#5
[20250402_082853.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082853.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)
[20250402_082853.]: Entered 'hyperbolic_regression'-Function
[20250402_082853.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082855.]: Entered 'cubic_regression'-Function
[20250402_082855.]: 'cubic_regression': minmax = FALSE
[20250402_082849.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250402_082849.]: Logging df_agg: CpG#6
[20250402_082849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082849.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)
[20250402_082849.]: Entered 'hyperbolic_regression'-Function
[20250402_082849.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082850.]: Entered 'cubic_regression'-Function
[20250402_082850.]: 'cubic_regression': minmax = FALSE
[20250402_082850.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250402_082850.]: Logging df_agg: CpG#7
[20250402_082850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082850.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)
[20250402_082850.]: Entered 'hyperbolic_regression'-Function
[20250402_082850.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082851.]: Entered 'cubic_regression'-Function
[20250402_082851.]: 'cubic_regression': minmax = FALSE
[20250402_082851.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250402_082851.]: Logging df_agg: CpG#8
[20250402_082851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082851.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)
[20250402_082851.]: Entered 'hyperbolic_regression'-Function
[20250402_082852.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082853.]: Entered 'cubic_regression'-Function
[20250402_082853.]: 'cubic_regression': minmax = FALSE
[20250402_082853.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250402_082853.]: Logging df_agg: CpG#9
[20250402_082853.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082853.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)
[20250402_082853.]: Entered 'hyperbolic_regression'-Function
[20250402_082853.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082854.]: Entered 'cubic_regression'-Function
[20250402_082854.]: 'cubic_regression': minmax = FALSE
[20250402_082854.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250402_082854.]: Logging df_agg: row_means
[20250402_082854.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082854.]: c(0.290941088603071, 11.0412408065783, 25.4081501047696, 36.5243719024532, 50.7348824329668, 65.3135209766198, 75.5342709041132, 83.2411228425212, 100)
[20250402_082854.]: Entered 'hyperbolic_regression'-Function
[20250402_082854.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082855.]: Entered 'cubic_regression'-Function
[20250402_082855.]: 'cubic_regression': minmax = FALSE
[20250402_082857.]: Entered 'solving_equations'-Function
[20250402_082857.]: Solving cubic regression for CpG#1
Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 0
Root: 1.334
--> Root in between the borders! Added to results.
Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 12.5
Root: 11.446
--> Root in between the borders! Added to results.
Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 25
Root: 22.228
--> Root in between the borders! Added to results.
Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 37.5
Root: 36.374
--> Root in between the borders! Added to results.
Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 50
Root: 52.044
--> Root in between the borders! Added to results.
Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 62.5
Root: 66.144
--> Root in between the borders! Added to results.
Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 75
Root: 75.864
--> Root in between the borders! Added to results.
Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 87.5
Root: 82.254
--> Root in between the borders! Added to results.
Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05
[20250402_082857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.877
--> '100 < root < 110' --> substitute 100
[20250402_082857.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082857.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#3
Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 0
Root: 1.441
--> Root in between the borders! Added to results.
Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 12.5
Root: 10.568
--> Root in between the borders! Added to results.
Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 25
Root: 24.796
--> Root in between the borders! Added to results.
Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 37.5
Root: 35.952
--> Root in between the borders! Added to results.
Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 50
Root: 50.684
--> Root in between the borders! Added to results.
Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 62.5
Root: 65.142
--> Root in between the borders! Added to results.
Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 75
Root: 77.905
--> Root in between the borders! Added to results.
Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 87.5
Root: 80.767
--> Root in between the borders! Added to results.
Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.38
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#4
Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 0
Root: 0.858
--> Root in between the borders! Added to results.
Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 12.5
Root: 12.086
--> Root in between the borders! Added to results.
Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 25
Root: 23.316
--> Root in between the borders! Added to results.
Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 37.5
Root: 37.383
--> Root in between the borders! Added to results.
Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 50
Root: 48.353
--> Root in between the borders! Added to results.
Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 62.5
Root: 68.082
--> Root in between the borders! Added to results.
Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 75
Root: 74.615
--> Root in between the borders! Added to results.
Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 87.5
Root: 83.254
--> Root in between the borders! Added to results.
Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.715
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#6
Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 0
Root: 0.349
--> Root in between the borders! Added to results.
Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 12.5
Root: 11.718
--> Root in between the borders! Added to results.
Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 25
Root: 26.396
--> Root in between the borders! Added to results.
Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 37.5
Root: 35.301
--> Root in between the borders! Added to results.
Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 50
Root: 50.134
--> Root in between the borders! Added to results.
Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 62.5
Root: 64.993
--> Root in between the borders! Added to results.
Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 75
Root: 73.639
--> Root in between the borders! Added to results.
Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 87.5
Root: 87.043
--> Root in between the borders! Added to results.
Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.329
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#7
Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 0
Root: 2.13
--> Root in between the borders! Added to results.
Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 12.5
Root: 9.973
--> Root in between the borders! Added to results.
Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 25
Root: 22.206
--> Root in between the borders! Added to results.
Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 37.5
Root: 35.814
--> Root in between the borders! Added to results.
Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 50
Root: 53.28
--> Root in between the borders! Added to results.
Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 62.5
Root: 66.598
--> Root in between the borders! Added to results.
Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 75
Root: 76.575
--> Root in between the borders! Added to results.
Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 87.5
Root: 80.219
--> Root in between the borders! Added to results.
Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.506
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250402_082858.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082858.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250402_082859.]: Solving cubic regression for row_means
Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 0
Root: 1.287
--> Root in between the borders! Added to results.
Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 12.5
Root: 10.847
--> Root in between the borders! Added to results.
Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 25
Root: 24.737
--> Root in between the borders! Added to results.
Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 37.5
Root: 36.02
--> Root in between the borders! Added to results.
Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 50
Root: 50.639
--> Root in between the borders! Added to results.
Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 62.5
Root: 65.497
--> Root in between the borders! Added to results.
Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 75
Root: 75.751
--> Root in between the borders! Added to results.
Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 87.5
Root: 83.403
--> Root in between the borders! Added to results.
Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05
[20250402_082859.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.573
--> '100 < root < 110' --> substitute 100
[20250402_082859.]: ### Starting with regression calculations ###
[20250402_082859.]: Entered 'regression_type1'-Function
[20250402_082903.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250402_082904.]: Logging df_agg: CpG#1
[20250402_082904.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082904.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)
[20250402_082904.]: Entered 'hyperbolic_regression'-Function
[20250402_082904.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082906.]: Entered 'cubic_regression'-Function
[20250402_082906.]: 'cubic_regression': minmax = FALSE
[20250402_082906.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250402_082906.]: Logging df_agg: CpG#2
[20250402_082906.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082906.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)
[20250402_082906.]: Entered 'hyperbolic_regression'-Function
[20250402_082906.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082907.]: Entered 'cubic_regression'-Function
[20250402_082907.]: 'cubic_regression': minmax = FALSE
[20250402_082907.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250402_082907.]: Logging df_agg: CpG#3
[20250402_082907.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082907.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)
[20250402_082907.]: Entered 'hyperbolic_regression'-Function
[20250402_082907.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082909.]: Entered 'cubic_regression'-Function
[20250402_082909.]: 'cubic_regression': minmax = FALSE
[20250402_082909.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250402_082909.]: Logging df_agg: CpG#4
[20250402_082909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082909.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)
[20250402_082909.]: Entered 'hyperbolic_regression'-Function
[20250402_082910.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082911.]: Entered 'cubic_regression'-Function
[20250402_082911.]: 'cubic_regression': minmax = FALSE
[20250402_082911.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250402_082911.]: Logging df_agg: CpG#5
[20250402_082911.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082911.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)
[20250402_082911.]: Entered 'hyperbolic_regression'-Function
[20250402_082911.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082912.]: Entered 'cubic_regression'-Function
[20250402_082912.]: 'cubic_regression': minmax = FALSE
[20250402_082905.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250402_082905.]: Logging df_agg: CpG#6
[20250402_082905.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082905.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)
[20250402_082905.]: Entered 'hyperbolic_regression'-Function
[20250402_082905.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082908.]: Entered 'cubic_regression'-Function
[20250402_082908.]: 'cubic_regression': minmax = FALSE
[20250402_082908.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250402_082908.]: Logging df_agg: CpG#7
[20250402_082908.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082908.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)
[20250402_082908.]: Entered 'hyperbolic_regression'-Function
[20250402_082908.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082909.]: Entered 'cubic_regression'-Function
[20250402_082909.]: 'cubic_regression': minmax = FALSE
[20250402_082909.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250402_082909.]: Logging df_agg: CpG#8
[20250402_082909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082909.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)
[20250402_082909.]: Entered 'hyperbolic_regression'-Function
[20250402_082909.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082909.]: Entered 'cubic_regression'-Function
[20250402_082909.]: 'cubic_regression': minmax = FALSE
[20250402_082909.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250402_082909.]: Logging df_agg: CpG#9
[20250402_082909.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082909.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)
[20250402_082909.]: Entered 'hyperbolic_regression'-Function
[20250402_082909.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082910.]: Entered 'cubic_regression'-Function
[20250402_082910.]: 'cubic_regression': minmax = FALSE
[20250402_082910.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250402_082910.]: Logging df_agg: row_means
[20250402_082910.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082910.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)
[20250402_082910.]: Entered 'hyperbolic_regression'-Function
[20250402_082910.]: 'hyperbolic_regression': minmax = FALSE
[20250402_082911.]: Entered 'cubic_regression'-Function
[20250402_082911.]: 'cubic_regression': minmax = FALSE
[20250402_082914.]: Entered 'solving_equations'-Function
[20250402_082914.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 79.8673456895745
[20250402_082914.]: Samplename: Sample#1
Root: 79.867
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.7900184340805
[20250402_082914.]: Samplename: Sample#10
Root: 29.79
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.6525415639691
[20250402_082914.]: Samplename: Sample#2
Root: 41.653
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.4652090254513
[20250402_082914.]: Samplename: Sample#3
Root: 57.465
--> Root in between the borders! Added to results.
Hyperbolic solved: 9.2007130627765
[20250402_082914.]: Samplename: Sample#4
Root: 9.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8059600538131
[20250402_082914.]: Samplename: Sample#5
Root: 21.806
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.083796735881
[20250402_082914.]: Samplename: Sample#6
Root: 23.084
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.5034245569385
[20250402_082914.]: Samplename: Sample#7
Root: 45.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6987904075704
[20250402_082914.]: Samplename: Sample#8
Root: 85.699
--> Root in between the borders! Added to results.
Hyperbolic solved: -3.66512807265101
[20250402_082914.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -3.665
--> '-10 < root < 0' --> substitute 0
[20250402_082914.]: Solving cubic regression for CpG#2
Coefficients: -60.0166632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082914.]: Samplename: Sample#1
Root: 76.388
--> Root in between the borders! Added to results.
Coefficients: -19.33132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#10
Root: 31.437
--> Root in between the borders! Added to results.
Coefficients: -28.1616632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#2
Root: 42.956
--> Root in between the borders! Added to results.
Coefficients: -42.07832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#3
Root: 58.838
--> Root in between the borders! Added to results.
Coefficients: -2.49332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#4
Root: 4.715
--> Root in between the borders! Added to results.
Coefficients: -11.94832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#5
Root: 20.644
--> Root in between the borders! Added to results.
Coefficients: -10.36332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#6
Root: 18.159
--> Root in between the borders! Added to results.
Coefficients: -26.77132996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#7
Root: 41.228
--> Root in between the borders! Added to results.
Coefficients: -70.81532996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#8
Root: 85.785
--> Root in between the borders! Added to results.
Coefficients: -1.41332996632996Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082915.]: Samplename: Sample#9
Root: 2.703
--> Root in between the borders! Added to results.
[20250402_082915.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.9349254100163
[20250402_082915.]: Samplename: Sample#1
Root: 74.935
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.6844381581493
[20250402_082915.]: Samplename: Sample#10
Root: 27.684
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.852019114379
[20250402_082915.]: Samplename: Sample#2
Root: 41.852
--> Root in between the borders! Added to results.
Hyperbolic solved: 55.8325180209418
[20250402_082915.]: Samplename: Sample#3
Root: 55.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.03519251633153
[20250402_082915.]: Samplename: Sample#4
Root: 8.035
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.1066315721853
[20250402_082915.]: Samplename: Sample#5
Root: 24.107
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2419820027673
[20250402_082915.]: Samplename: Sample#6
Root: 26.242
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0944922703422
[20250402_082915.]: Samplename: Sample#7
Root: 44.094
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.8279382585787
[20250402_082915.]: Samplename: Sample#8
Root: 85.828
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.666482392725758
[20250402_082915.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.666
--> '-10 < root < 0' --> substitute 0
[20250402_082915.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 76.3495278640236
[20250402_082915.]: Samplename: Sample#1
Root: 76.35
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.2568553570941
[20250402_082915.]: Samplename: Sample#10
Root: 28.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4089839390807
[20250402_082915.]: Samplename: Sample#2
Root: 43.409
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.5435236860146
[20250402_082915.]: Samplename: Sample#3
Root: 58.544
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.3087045690571
[20250402_082915.]: Samplename: Sample#4
Root: 10.309
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.183045165659
[20250402_082915.]: Samplename: Sample#5
Root: 22.183
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.1337769553499
[20250402_082915.]: Samplename: Sample#6
Root: 27.134
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8321096080155
[20250402_082915.]: Samplename: Sample#7
Root: 41.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6890189074743
[20250402_082915.]: Samplename: Sample#8
Root: 85.689
--> Root in between the borders! Added to results.
Hyperbolic solved: 2.42232098177269
[20250402_082915.]: Samplename: Sample#9
Root: 2.422
--> Root in between the borders! Added to results.
[20250402_082916.]: Solving cubic regression for CpG#5
Coefficients: -48.4612946127946Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -14.2119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#10
Root: 27.256
--> Root in between the borders! Added to results.
Coefficients: -25.9451041366041Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#2
Root: 44.648
--> Root in between the borders! Added to results.
Coefficients: -32.6879612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#3
Root: 53.538
--> Root in between the borders! Added to results.
Coefficients: -4.69796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#4
Root: 10.206
--> Root in between the borders! Added to results.
Coefficients: -12.0579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#5
Root: 23.695
--> Root in between the borders! Added to results.
Coefficients: -13.9179612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#6
Root: 26.778
--> Root in between the borders! Added to results.
Coefficients: -24.9119612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#7
Root: 43.226
--> Root in between the borders! Added to results.
Coefficients: -63.7579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#8
Root: 88.581
--> Root in between the borders! Added to results.
Coefficients: -0.587961279461277Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082916.]: Samplename: Sample#9
Root: 1.375
--> Root in between the borders! Added to results.
[20250402_082916.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2780593622711
[20250402_082916.]: Samplename: Sample#1
Root: 79.278
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2012458984074
[20250402_082916.]: Samplename: Sample#10
Root: 30.201
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.8474393624107
[20250402_082916.]: Samplename: Sample#2
Root: 41.847
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8423517321508
[20250402_082916.]: Samplename: Sample#3
Root: 56.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.87856046118588
[20250402_082916.]: Samplename: Sample#4
Root: 8.879
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.69015950004
[20250402_082916.]: Samplename: Sample#5
Root: 18.69
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9309263534749
[20250402_082916.]: Samplename: Sample#6
Root: 29.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8148560027697
[20250402_082916.]: Samplename: Sample#7
Root: 42.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.7501831416152
[20250402_082916.]: Samplename: Sample#8
Root: 86.75
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.51516194985267
[20250402_082916.]: Samplename: Sample#9
Root: 1.515
--> Root in between the borders! Added to results.
[20250402_082916.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 78.2565592569279
[20250402_082916.]: Samplename: Sample#1
Root: 78.257
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.488739349283
[20250402_082916.]: Samplename: Sample#10
Root: 25.489
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.3712258915285
[20250402_082916.]: Samplename: Sample#2
Root: 47.371
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.3142673189298
[20250402_082916.]: Samplename: Sample#3
Root: 58.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7212231360573
[20250402_082916.]: Samplename: Sample#4
Root: 11.721
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.3797485992238
[20250402_082916.]: Samplename: Sample#5
Root: 25.38
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.4095133062523
[20250402_082916.]: Samplename: Sample#6
Root: 29.41
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.5755071469546
[20250402_082916.]: Samplename: Sample#7
Root: 44.576
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.9628731021447
[20250402_082916.]: Samplename: Sample#8
Root: 85.963
--> Root in between the borders! Added to results.
Hyperbolic solved: -4.1645647175353
[20250402_082916.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.165
--> '-10 < root < 0' --> substitute 0
[20250402_082916.]: Solving cubic regression for CpG#8
Coefficients: -56.4535185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#1
Root: 72.337
--> Root in between the borders! Added to results.
Coefficients: -18.6701851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#10
Root: 28.678
--> Root in between the borders! Added to results.
Coefficients: -24.0387566137566Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#2
Root: 35.595
--> Root in between the borders! Added to results.
Coefficients: -43.9451851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#3
Root: 58.861
--> Root in between the borders! Added to results.
Coefficients: -5.70018518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#4
Root: 9.868
--> Root in between the borders! Added to results.
Coefficients: -12.4851851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#5
Root: 20.166
--> Root in between the borders! Added to results.
Coefficients: -26.8801851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#6
Root: 39.117
--> Root in between the borders! Added to results.
Coefficients: -31.8421851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#7
Root: 45.08
--> Root in between the borders! Added to results.
Coefficients: -68.0081851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#8
Root: 84.373
--> Root in between the borders! Added to results.
Coefficients: 2.07981481481482Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082916.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -4.026
--> '-10 < root < 0' --> substitute 0
[20250402_082916.]: Solving cubic regression for CpG#9
Coefficients: -60.8091986531987Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#1
Root: 81.262
--> Root in between the borders! Added to results.
Coefficients: -14.5538653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#10
Root: 24.569
--> Root in between the borders! Added to results.
Coefficients: -26.6344367484368Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#2
Root: 45.035
--> Root in between the borders! Added to results.
Coefficients: -35.4783653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#3
Root: 57.113
--> Root in between the borders! Added to results.
Coefficients: -4.73586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#4
Root: 7.362
--> Root in between the borders! Added to results.
Coefficients: -12.5308653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082916.]: Samplename: Sample#5
Root: 20.907
--> Root in between the borders! Added to results.
Coefficients: -21.9358653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082917.]: Samplename: Sample#6
Root: 37.545
--> Root in between the borders! Added to results.
Coefficients: -25.1998653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082917.]: Samplename: Sample#7
Root: 42.828
--> Root in between the borders! Added to results.
Coefficients: -70.5118653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082917.]: Samplename: Sample#8
Root: 88.082
--> Root in between the borders! Added to results.
Coefficients: -0.505865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082917.]: Samplename: Sample#9
Root: 0.749
--> Root in between the borders! Added to results.
[20250402_082917.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 77.0692797356261
[20250402_082917.]: Samplename: Sample#1
Root: 77.069
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3620040447844
[20250402_082917.]: Samplename: Sample#10
Root: 28.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.5026170660315
[20250402_082918.]: Samplename: Sample#2
Root: 42.503
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.2972045344154
[20250402_082918.]: Samplename: Sample#3
Root: 57.297
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.82704040274281
[20250402_082918.]: Samplename: Sample#4
Root: 8.827
--> Root in between the borders! Added to results.
Hyperbolic solved: 21.8102591233667
[20250402_082918.]: Samplename: Sample#5
Root: 21.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.722865717687
[20250402_082918.]: Samplename: Sample#6
Root: 28.723
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.4105098027891
[20250402_082918.]: Samplename: Sample#7
Root: 43.411
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.4143551699061
[20250402_082918.]: Samplename: Sample#8
Root: 86.414
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.237019926848022
[20250402_082918.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.237
--> '-10 < root < 0' --> substitute 0
[20250402_082918.]: Entered 'solving_equations'-Function
[20250402_082918.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: -2.23222990163966
[20250402_082918.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.232
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.1698489850618
[20250402_082918.]: Samplename: 12.5
Root: 12.17
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.4781920312644
[20250402_082918.]: Samplename: 25
Root: 24.478
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.173044740918
[20250402_082918.]: Samplename: 37.5
Root: 38.173
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3349371964438
[20250402_082918.]: Samplename: 50
Root: 52.335
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4582773627666
[20250402_082918.]: Samplename: 62.5
Root: 65.458
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.0090795260796
[20250402_082918.]: Samplename: 75
Root: 75.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.5271920968417
[20250402_082918.]: Samplename: 87.5
Root: 81.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.400893095062
[20250402_082918.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.401
--> '100 < root < 110' --> substitute 100
[20250402_082918.]: Solving cubic regression for CpG#2
Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 0
Root: 0.549
--> Root in between the borders! Added to results.
Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 12.5
Root: 11.533
--> Root in between the borders! Added to results.
Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 25
Root: 26.628
--> Root in between the borders! Added to results.
Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 37.5
Root: 35.509
--> Root in between the borders! Added to results.
Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 50
Root: 47.851
--> Root in between the borders! Added to results.
Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 62.5
Root: 66.893
--> Root in between the borders! Added to results.
Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 75
Root: 75.431
--> Root in between the borders! Added to results.
Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 87.5
Root: 84.118
--> Root in between the borders! Added to results.
Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06
[20250402_082918.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.287
--> '100 < root < 110' --> substitute 100
[20250402_082918.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0.51235653688495
[20250402_082918.]: Samplename: 0
Root: 0.512
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7523884294604
[20250402_082918.]: Samplename: 12.5
Root: 10.752
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.5218907947761
[20250402_082918.]: Samplename: 25
Root: 25.522
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5270462675211
[20250402_082918.]: Samplename: 37.5
Root: 36.527
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7909245028224
[20250402_082918.]: Samplename: 50
Root: 50.791
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8686317550184
[20250402_082918.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 77.5524188495235
[20250402_082918.]: Samplename: 75
Root: 77.552
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.4374617358174
[20250402_082918.]: Samplename: 87.5
Root: 80.437
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.704024900825
[20250402_082918.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.704
--> '100 < root < 110' --> substitute 100
[20250402_082918.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: -0.519503092357606
[20250402_082918.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.52
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 12.4934147844872
[20250402_082918.]: Samplename: 12.5
Root: 12.493
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2685420024115
[20250402_082918.]: Samplename: 25
Root: 24.269
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.0817128465023
[20250402_082918.]: Samplename: 37.5
Root: 38.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.5843181174811
[20250402_082918.]: Samplename: 50
Root: 48.584
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.6722399183037
[20250402_082918.]: Samplename: 62.5
Root: 67.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.1549277799119
[20250402_082918.]: Samplename: 75
Root: 74.155
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.8821797890026
[20250402_082918.]: Samplename: 87.5
Root: 82.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 102.0791269023
[20250402_082918.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.079
--> '100 < root < 110' --> substitute 100
[20250402_082918.]: Solving cubic regression for CpG#5
Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 0
Root: 1.458
--> Root in between the borders! Added to results.
Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 12.5
Root: 10.347
--> Root in between the borders! Added to results.
Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 25
Root: 24.815
--> Root in between the borders! Added to results.
Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 37.5
Root: 37.902
--> Root in between the borders! Added to results.
Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 50
Root: 50.977
--> Root in between the borders! Added to results.
Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 62.5
Root: 62.126
--> Root in between the borders! Added to results.
Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082918.]: Samplename: 75
Root: 75.852
--> Root in between the borders! Added to results.
Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082919.]: Samplename: 87.5
Root: 85.767
--> Root in between the borders! Added to results.
Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06
[20250402_082919.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.743
--> '100 < root < 110' --> substitute 100
[20250402_082919.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0.138163748613034
[20250402_082919.]: Samplename: 0
Root: 0.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.8635558881981
[20250402_082919.]: Samplename: 12.5
Root: 11.864
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5107449550797
[20250402_082919.]: Samplename: 25
Root: 26.511
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3205073050661
[20250402_082919.]: Samplename: 37.5
Root: 35.321
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.0570767570666
[20250402_082919.]: Samplename: 50
Root: 50.057
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9602944381018
[20250402_082919.]: Samplename: 62.5
Root: 64.96
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.66890571617
[20250402_082919.]: Samplename: 75
Root: 73.669
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.1266086585036
[20250402_082919.]: Samplename: 87.5
Root: 87.127
--> Root in between the borders! Added to results.
Hyperbolic solved: 100.261637014212
[20250402_082919.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100.262
--> '100 < root < 110' --> substitute 100
[20250402_082919.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: -1.37238087287012
[20250402_082919.]: Samplename: 0
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.372
--> '-10 < root < 0' --> substitute 0
Hyperbolic solved: 10.1993162352498
[20250402_082919.]: Samplename: 12.5
Root: 10.199
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.595178967123
[20250402_082919.]: Samplename: 25
Root: 24.595
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.8310421041787
[20250402_082919.]: Samplename: 37.5
Root: 37.831
--> Root in between the borders! Added to results.
Hyperbolic solved: 53.5588739724067
[20250402_082919.]: Samplename: 50
Root: 53.559
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.9364947980258
[20250402_082919.]: Samplename: 62.5
Root: 65.936
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.7361094434913
[20250402_082919.]: Samplename: 75
Root: 75.736
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.432823759854
[20250402_082919.]: Samplename: 87.5
Root: 79.433
--> Root in between the borders! Added to results.
Hyperbolic solved: 103.004237013737
[20250402_082919.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 103.004
--> '100 < root < 110' --> substitute 100
[20250402_082919.]: Solving cubic regression for CpG#8
Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 0
Root: 2.016
--> Root in between the borders! Added to results.
Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 12.5
Root: 9.458
--> Root in between the borders! Added to results.
Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 25
Root: 26.35
--> Root in between the borders! Added to results.
Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 37.5
Root: 34.728
--> Root in between the borders! Added to results.
Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 50
Root: 51.781
--> Root in between the borders! Added to results.
Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 62.5
Root: 64.192
--> Root in between the borders! Added to results.
Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 75
Root: 77.804
--> Root in between the borders! Added to results.
Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 87.5
Root: 80.758
--> Root in between the borders! Added to results.
Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05
[20250402_082919.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 102.834
--> '100 < root < 110' --> substitute 100
[20250402_082919.]: Solving cubic regression for CpG#9
Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 0
Root: 1.475
--> Root in between the borders! Added to results.
Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 12.5
Root: 10.12
--> Root in between the borders! Added to results.
Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 25
Root: 24.844
--> Root in between the borders! Added to results.
Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 37.5
Root: 35.327
--> Root in between the borders! Added to results.
Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 50
Root: 51.855
--> Root in between the borders! Added to results.
Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 62.5
Root: 65.265
--> Root in between the borders! Added to results.
Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 75
Root: 74.915
--> Root in between the borders! Added to results.
Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 87.5
Root: 84.67
--> Root in between the borders! Added to results.
Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05
[20250402_082919.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.082
--> '100 < root < 110' --> substitute 100
[20250402_082919.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0.290941088603071
[20250402_082919.]: Samplename: 0
Root: 0.291
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0412408065783
[20250402_082919.]: Samplename: 12.5
Root: 11.041
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.4081501047696
[20250402_082919.]: Samplename: 25
Root: 25.408
--> Root in between the borders! Added to results.
Hyperbolic solved: 36.5243719024532
[20250402_082919.]: Samplename: 37.5
Root: 36.524
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.7348824329668
[20250402_082919.]: Samplename: 50
Root: 50.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.3135209766198
[20250402_082919.]: Samplename: 62.5
Root: 65.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.5342709041132
[20250402_082919.]: Samplename: 75
Root: 75.534
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.2411228425212
[20250402_082920.]: Samplename: 87.5
Root: 83.241
--> Root in between the borders! Added to results.
Hyperbolic solved: 101.666942781592
[20250402_082920.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 101.667
--> '100 < root < 110' --> substitute 100
[20250402_082923.]: Entered 'clean_dt'-Function
[20250402_082923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082923.]: got experimental data
[20250402_082923.]: Entered 'clean_dt'-Function
[20250402_082923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_082923.]: got calibration data
[20250402_082923.]: ### Starting with regression calculations ###
[20250402_082924.]: Entered 'regression_type1'-Function
[20250402_082925.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082925.]: Logging df_agg: CpG#1
[20250402_082925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082925.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082925.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082925.]: Entered 'hyperbolic_regression'-Function
[20250402_082925.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082927.]: Entered 'cubic_regression'-Function
[20250402_082927.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082928.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082928.]: Logging df_agg: CpG#2
[20250402_082928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082928.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082928.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082928.]: Entered 'hyperbolic_regression'-Function
[20250402_082928.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082930.]: Entered 'cubic_regression'-Function
[20250402_082930.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082931.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082931.]: Logging df_agg: CpG#3
[20250402_082931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082931.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082931.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082931.]: Entered 'hyperbolic_regression'-Function
[20250402_082931.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082933.]: Entered 'cubic_regression'-Function
[20250402_082933.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082934.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082934.]: Logging df_agg: CpG#4
[20250402_082934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082934.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082934.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082934.]: Entered 'hyperbolic_regression'-Function
[20250402_082934.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082935.]: Entered 'cubic_regression'-Function
[20250402_082935.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082936.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082936.]: Logging df_agg: CpG#5
[20250402_082936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082936.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_082936.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_082936.]: Entered 'hyperbolic_regression'-Function
[20250402_082936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082937.]: Entered 'cubic_regression'-Function
[20250402_082937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082927.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082927.]: Logging df_agg: CpG#6
[20250402_082927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082927.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082927.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082927.]: Entered 'hyperbolic_regression'-Function
[20250402_082927.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082929.]: Entered 'cubic_regression'-Function
[20250402_082929.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082930.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082930.]: Logging df_agg: CpG#7
[20250402_082930.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082930.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082930.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082930.]: Entered 'hyperbolic_regression'-Function
[20250402_082930.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082931.]: Entered 'cubic_regression'-Function
[20250402_082931.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082932.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082932.]: Logging df_agg: CpG#8
[20250402_082932.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082932.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082932.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082932.]: Entered 'hyperbolic_regression'-Function
[20250402_082932.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082934.]: Entered 'cubic_regression'-Function
[20250402_082934.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082934.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082934.]: Logging df_agg: CpG#9
[20250402_082934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082934.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082934.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082934.]: Entered 'hyperbolic_regression'-Function
[20250402_082934.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082935.]: Entered 'cubic_regression'-Function
[20250402_082935.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082936.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082936.]: Logging df_agg: row_means
[20250402_082936.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082936.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082936.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082936.]: Entered 'hyperbolic_regression'-Function
[20250402_082936.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082937.]: Entered 'cubic_regression'-Function
[20250402_082937.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082949.]: Entered 'regression_type1'-Function
[20250402_082950.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082951.]: Logging df_agg: CpG#1
[20250402_082951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082951.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_082951.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_082951.]: Entered 'hyperbolic_regression'-Function
[20250402_082951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082953.]: Entered 'cubic_regression'-Function
[20250402_082953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082954.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082955.]: Logging df_agg: CpG#2
[20250402_082955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082955.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_082955.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_082955.]: Entered 'hyperbolic_regression'-Function
[20250402_082955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082956.]: Entered 'cubic_regression'-Function
[20250402_082956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082957.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082957.]: Logging df_agg: CpG#3
[20250402_082957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082957.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_082957.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_082957.]: Entered 'hyperbolic_regression'-Function
[20250402_082957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082958.]: Entered 'cubic_regression'-Function
[20250402_082958.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082959.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082959.]: Logging df_agg: CpG#4
[20250402_082959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082959.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_082959.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_082959.]: Entered 'hyperbolic_regression'-Function
[20250402_082959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083000.]: Entered 'cubic_regression'-Function
[20250402_083000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083001.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083001.]: Logging df_agg: CpG#5
[20250402_083001.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083001.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_083001.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083001.]: Entered 'hyperbolic_regression'-Function
[20250402_083001.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083003.]: Entered 'cubic_regression'-Function
[20250402_083003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082951.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082952.]: Logging df_agg: CpG#6
[20250402_082952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082952.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_082952.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_082952.]: Entered 'hyperbolic_regression'-Function
[20250402_082952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082953.]: Entered 'cubic_regression'-Function
[20250402_082953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082953.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082954.]: Logging df_agg: CpG#7
[20250402_082954.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082954.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_082954.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_082954.]: Entered 'hyperbolic_regression'-Function
[20250402_082954.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082954.]: Entered 'cubic_regression'-Function
[20250402_082954.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082956.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082956.]: Logging df_agg: CpG#8
[20250402_082956.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082956.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_082956.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_082956.]: Entered 'hyperbolic_regression'-Function
[20250402_082956.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082957.]: Entered 'cubic_regression'-Function
[20250402_082957.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082958.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082958.]: Logging df_agg: CpG#9
[20250402_082958.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082958.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_082958.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_082958.]: Entered 'hyperbolic_regression'-Function
[20250402_082958.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082959.]: Entered 'cubic_regression'-Function
[20250402_082959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_082959.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082959.]: Logging df_agg: row_means
[20250402_082959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_082959.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_082959.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_082959.]: Entered 'hyperbolic_regression'-Function
[20250402_082959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083001.]: Entered 'cubic_regression'-Function
[20250402_083001.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083009.]: Entered 'clean_dt'-Function
[20250402_083009.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_083009.]: got experimental data
[20250402_083009.]: Entered 'clean_dt'-Function
[20250402_083009.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_083009.]: got calibration data
[20250402_083009.]: ### Starting with regression calculations ###
[20250402_083009.]: Entered 'regression_type1'-Function
[20250402_083011.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_083011.]: Logging df_agg: CpG#1
[20250402_083011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083011.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_083011.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_083011.]: Entered 'hyperbolic_regression'-Function
[20250402_083011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083013.]: Entered 'cubic_regression'-Function
[20250402_083013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083013.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_083013.]: Logging df_agg: CpG#2
[20250402_083013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083013.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_083013.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_083013.]: Entered 'hyperbolic_regression'-Function
[20250402_083013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083014.]: Entered 'cubic_regression'-Function
[20250402_083014.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083015.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_083015.]: Logging df_agg: CpG#3
[20250402_083015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083015.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_083015.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_083015.]: Entered 'hyperbolic_regression'-Function
[20250402_083015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083016.]: Entered 'cubic_regression'-Function
[20250402_083016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083017.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_083017.]: Logging df_agg: CpG#4
[20250402_083017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083017.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_083017.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_083017.]: Entered 'hyperbolic_regression'-Function
[20250402_083017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083018.]: Entered 'cubic_regression'-Function
[20250402_083018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083018.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083018.]: Logging df_agg: CpG#5
[20250402_083018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083018.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_083018.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083018.]: Entered 'hyperbolic_regression'-Function
[20250402_083018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083020.]: Entered 'cubic_regression'-Function
[20250402_083020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083013.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_083013.]: Logging df_agg: CpG#6
[20250402_083013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083013.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_083013.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_083013.]: Entered 'hyperbolic_regression'-Function
[20250402_083013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083014.]: Entered 'cubic_regression'-Function
[20250402_083014.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083014.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_083014.]: Logging df_agg: CpG#7
[20250402_083014.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083014.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_083014.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_083014.]: Entered 'hyperbolic_regression'-Function
[20250402_083014.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083015.]: Entered 'cubic_regression'-Function
[20250402_083015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083015.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_083015.]: Logging df_agg: CpG#8
[20250402_083015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083015.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_083015.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_083015.]: Entered 'hyperbolic_regression'-Function
[20250402_083015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083017.]: Entered 'cubic_regression'-Function
[20250402_083017.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083017.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_083017.]: Logging df_agg: CpG#9
[20250402_083017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083017.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_083017.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_083017.]: Entered 'hyperbolic_regression'-Function
[20250402_083017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083018.]: Entered 'cubic_regression'-Function
[20250402_083018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083018.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_083018.]: Logging df_agg: row_means
[20250402_083018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083018.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_083018.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_083018.]: Entered 'hyperbolic_regression'-Function
[20250402_083018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083019.]: Entered 'cubic_regression'-Function
[20250402_083019.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083029.]: Entered 'regression_type1'-Function
[20250402_083031.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_083033.]: Logging df_agg: CpG#1
[20250402_083033.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083033.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250402_083033.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)
[20250402_083033.]: Entered 'hyperbolic_regression'-Function
[20250402_083033.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083034.]: Entered 'cubic_regression'-Function
[20250402_083034.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083035.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_083035.]: Logging df_agg: CpG#2
[20250402_083035.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083035.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250402_083035.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)
[20250402_083035.]: Entered 'hyperbolic_regression'-Function
[20250402_083035.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083036.]: Entered 'cubic_regression'-Function
[20250402_083036.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083037.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_083037.]: Logging df_agg: CpG#3
[20250402_083037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083037.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250402_083037.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)
[20250402_083037.]: Entered 'hyperbolic_regression'-Function
[20250402_083037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083038.]: Entered 'cubic_regression'-Function
[20250402_083038.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083038.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_083038.]: Logging df_agg: CpG#4
[20250402_083038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083038.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250402_083038.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)
[20250402_083038.]: Entered 'hyperbolic_regression'-Function
[20250402_083039.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083040.]: Entered 'cubic_regression'-Function
[20250402_083040.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083041.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083041.]: Logging df_agg: CpG#5
[20250402_083041.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083041.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250402_083041.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)
[20250402_083041.]: Entered 'hyperbolic_regression'-Function
[20250402_083041.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083042.]: Entered 'cubic_regression'-Function
[20250402_083042.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083033.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_083034.]: Logging df_agg: CpG#6
[20250402_083034.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083034.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250402_083034.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)
[20250402_083034.]: Entered 'hyperbolic_regression'-Function
[20250402_083034.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083035.]: Entered 'cubic_regression'-Function
[20250402_083035.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083035.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_083035.]: Logging df_agg: CpG#7
[20250402_083035.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083035.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250402_083035.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)
[20250402_083035.]: Entered 'hyperbolic_regression'-Function
[20250402_083035.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083036.]: Entered 'cubic_regression'-Function
[20250402_083037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083037.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_083037.]: Logging df_agg: CpG#8
[20250402_083037.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083037.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250402_083037.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)
[20250402_083037.]: Entered 'hyperbolic_regression'-Function
[20250402_083037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083038.]: Entered 'cubic_regression'-Function
[20250402_083038.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083039.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_083039.]: Logging df_agg: CpG#9
[20250402_083039.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083039.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250402_083039.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)
[20250402_083039.]: Entered 'hyperbolic_regression'-Function
[20250402_083039.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083040.]: Entered 'cubic_regression'-Function
[20250402_083040.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083041.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_083041.]: Logging df_agg: row_means
[20250402_083041.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083041.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250402_083041.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)
[20250402_083041.]: Entered 'hyperbolic_regression'-Function
[20250402_083041.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083042.]: Entered 'cubic_regression'-Function
[20250402_083042.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083045.]: Entered 'solving_equations'-Function
[20250402_083045.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250402_083045.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250402_083045.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250402_083045.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250402_083045.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250402_083045.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250402_083045.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250402_083045.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250402_083045.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#2
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.7851657015183
[20250402_083046.]: Samplename: 12.5
Root: 10.785
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.0727152156421
[20250402_083046.]: Samplename: 25
Root: 26.073
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.2074258210424
[20250402_083046.]: Samplename: 37.5
Root: 35.207
--> Root in between the borders! Added to results.
Hyperbolic solved: 47.9305924748583
[20250402_083046.]: Samplename: 50
Root: 47.931
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.2847555363015
[20250402_083046.]: Samplename: 62.5
Root: 67.285
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.735332403378
[20250402_083046.]: Samplename: 75
Root: 75.735
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.1313047876192
[20250402_083046.]: Samplename: 87.5
Root: 84.131
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250402_083046.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250402_083046.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250402_083046.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250402_083046.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250402_083046.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250402_083046.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250402_083046.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250402_083046.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250402_083046.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250402_083046.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250402_083046.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250402_083046.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250402_083046.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250402_083046.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#5
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.36665146544904
[20250402_083046.]: Samplename: 12.5
Root: 8.367
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.0855280383989
[20250402_083046.]: Samplename: 25
Root: 23.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0098400819818
[20250402_083046.]: Samplename: 37.5
Root: 37.01
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.0085868408378
[20250402_083046.]: Samplename: 50
Root: 51.009
--> Root in between the borders! Added to results.
Hyperbolic solved: 62.7441416833696
[20250402_083046.]: Samplename: 62.5
Root: 62.744
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.6857826005162
[20250402_083046.]: Samplename: 75
Root: 76.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.3046084696663
[20250402_083046.]: Samplename: 87.5
Root: 86.305
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250402_083046.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250402_083046.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250402_083046.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250402_083046.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250402_083046.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250402_083046.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250402_083046.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250402_083046.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250402_083046.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250402_083046.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250402_083046.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250402_083046.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250402_083046.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250402_083046.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#8
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.27736114274885
[20250402_083046.]: Samplename: 12.5
Root: 7.277
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.9863834890886
[20250402_083046.]: Samplename: 25
Root: 24.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 34.0400823094579
[20250402_083046.]: Samplename: 37.5
Root: 34.04
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3077192847199
[20250402_083046.]: Samplename: 50
Root: 52.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0861558866387
[20250402_083046.]: Samplename: 62.5
Root: 65.086
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.3136588178128
[20250402_083046.]: Samplename: 75
Root: 78.314
--> Root in between the borders! Added to results.
Hyperbolic solved: 81.058248740059
[20250402_083046.]: Samplename: 87.5
Root: 81.058
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250402_083046.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250402_083046.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250402_083046.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250402_083046.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250402_083046.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250402_083046.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250402_083046.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250402_083046.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250402_083046.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250402_083046.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250402_083046.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250402_083046.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250402_083046.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250402_083046.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250402_083046.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083046.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083046.]: ### Starting with regression calculations ###
[20250402_083046.]: Entered 'regression_type1'-Function
[20250402_083050.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250402_083051.]: Logging df_agg: CpG#1
[20250402_083051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083051.]: c(0, 14.1381159662486, 26.1241053609707, 39.3567419170867, 52.9273107806133, 65.4010628999278, 74.4183184249663, 80.5431520527512, 100)
[20250402_083051.]: Entered 'hyperbolic_regression'-Function
[20250402_083051.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083052.]: Entered 'cubic_regression'-Function
[20250402_083052.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083053.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250402_083053.]: Logging df_agg: CpG#2
[20250402_083053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083053.]: c(0, 10.7851657015183, 26.0727152156421, 35.2074258210424, 47.9305924748583, 67.2847555363015, 75.735332403378, 84.1313047876192, 100)
[20250402_083053.]: Entered 'hyperbolic_regression'-Function
[20250402_083053.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083054.]: Entered 'cubic_regression'-Function
[20250402_083054.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083055.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250402_083055.]: Logging df_agg: CpG#3
[20250402_083055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083055.]: c(0, 10.8497990553835, 26.1511183533449, 37.2940213300522, 51.419361136507, 65.0212050873619, 76.9977789568509, 79.686036177122, 100)
[20250402_083055.]: Entered 'hyperbolic_regression'-Function
[20250402_083055.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083056.]: Entered 'cubic_regression'-Function
[20250402_083056.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083057.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250402_083057.]: Logging df_agg: CpG#4
[20250402_083057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083057.]: c(0, 13.2434477796981, 25.0815867666892, 38.7956859187734, 49.1001600195185, 67.5620415214226, 73.7554076043322, 82.0327440839301, 100)
[20250402_083057.]: Entered 'hyperbolic_regression'-Function
[20250402_083057.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083057.]: Entered 'cubic_regression'-Function
[20250402_083057.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083058.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250402_083058.]: Logging df_agg: CpG#5
[20250402_083058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083058.]: c(0, 8.36665146544904, 23.0855280383989, 37.0098400819818, 51.0085868408378, 62.7441416833696, 76.6857826005162, 86.3046084696663, 100)
[20250402_083058.]: Entered 'hyperbolic_regression'-Function
[20250402_083058.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083059.]: Entered 'cubic_regression'-Function
[20250402_083059.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083052.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250402_083052.]: Logging df_agg: CpG#6
[20250402_083052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083052.]: c(0, 11.822687731114, 26.5494368772504, 35.3846787677878, 50.1264563333089, 64.9875101866844, 73.6494948240195, 87.0033714659226, 100)
[20250402_083052.]: Entered 'hyperbolic_regression'-Function
[20250402_083052.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083054.]: Entered 'cubic_regression'-Function
[20250402_083054.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083055.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250402_083055.]: Logging df_agg: CpG#7
[20250402_083055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083055.]: c(0, 11.7925453863418, 26.2042827174053, 39.2081609373531, 54.3620766326312, 66.0664882334621, 75.1981507250883, 78.6124357632637, 100)
[20250402_083055.]: Entered 'hyperbolic_regression'-Function
[20250402_083055.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083056.]: Entered 'cubic_regression'-Function
[20250402_083056.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083057.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250402_083057.]: Logging df_agg: CpG#8
[20250402_083057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083057.]: c(0, 7.27736114274885, 24.9863834890886, 34.0400823094579, 52.3077192847199, 65.0861558866387, 78.3136588178128, 81.058248740059, 100)
[20250402_083057.]: Entered 'hyperbolic_regression'-Function
[20250402_083057.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083059.]: Entered 'cubic_regression'-Function
[20250402_083059.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083059.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250402_083059.]: Logging df_agg: CpG#9
[20250402_083059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083059.]: c(0, 12.2094906593745, 28.0738986154201, 37.6720254587223, 52.3746308870569, 64.8693631845077, 74.2598902601534, 83.9376844048195, 100)
[20250402_083059.]: Entered 'hyperbolic_regression'-Function
[20250402_083059.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083101.]: Entered 'cubic_regression'-Function
[20250402_083101.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083101.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250402_083101.]: Logging df_agg: row_means
[20250402_083101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083101.]: c(0, 11.1506882890389, 25.841636381907, 37.0462679509085, 51.1681297765954, 65.4258217891781, 75.285632789037, 82.6475419323379, 100)
[20250402_083101.]: Entered 'hyperbolic_regression'-Function
[20250402_083102.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083102.]: Entered 'cubic_regression'-Function
[20250402_083102.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083106.]: Entered 'solving_equations'-Function
[20250402_083106.]: Solving cubic regression for CpG#1
Coefficients: 0Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.30533333333333Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 12.5
Root: 10.279
--> Root in between the borders! Added to results.
Coefficients: -14.352Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 25
Root: 21.591
--> Root in between the borders! Added to results.
Coefficients: -23.244Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 37.5
Root: 36.617
--> Root in between the borders! Added to results.
Coefficients: -33.8645Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 50
Root: 52.729
--> Root in between the borders! Added to results.
Coefficients: -45.318Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 62.5
Root: 66.532
--> Root in between the borders! Added to results.
Coefficients: -54.857Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 75
Root: 75.773
--> Root in between the borders! Added to results.
Coefficients: -62.062Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 87.5
Root: 81.772
--> Root in between the borders! Added to results.
Coefficients: -90.01Coefficients: 0.769986404107641Coefficients: -0.00657663513476353Coefficients: 7.87777109368712e-05
[20250402_083106.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083106.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083106.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083106.]: Solving cubic regression for CpG#3
Coefficients: 0Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.67Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 12.5
Root: 9.387
--> Root in between the borders! Added to results.
Coefficients: -14.526Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 25
Root: 24.373
--> Root in between the borders! Added to results.
Coefficients: -21.71Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 37.5
Root: 36.135
--> Root in between the borders! Added to results.
Coefficients: -31.8725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 50
Root: 51.29
--> Root in between the borders! Added to results.
Coefficients: -42.986Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 62.5
Root: 65.561
--> Root in between the borders! Added to results.
Coefficients: -54.0725Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 75
Root: 77.683
--> Root in between the borders! Added to results.
Coefficients: -56.7533333333333Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 87.5
Root: 80.348
--> Root in between the borders! Added to results.
Coefficients: -79.762Coefficients: 0.617172193771691Coefficients: -0.00173040359673478Coefficients: 3.53488165901787e-05
[20250402_083106.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083107.]: Solving cubic regression for CpG#4
Coefficients: 0Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -7.65533333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 12.5
Root: 11.333
--> Root in between the borders! Added to results.
Coefficients: -15.206Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 25
Root: 22.933
--> Root in between the borders! Added to results.
Coefficients: -24.93Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 37.5
Root: 37.542
--> Root in between the borders! Added to results.
Coefficients: -33.0395Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 50
Root: 48.772
--> Root in between the borders! Added to results.
Coefficients: -49.658Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 62.5
Root: 68.324
--> Root in between the borders! Added to results.
Coefficients: -55.942Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 75
Root: 74.614
--> Root in between the borders! Added to results.
Coefficients: -64.9953333333333Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 87.5
Root: 82.816
--> Root in between the borders! Added to results.
Coefficients: -87.724Coefficients: 0.698880117659818Coefficients: -0.00255689967362793Coefficients: 4.34049849702976e-05
[20250402_083107.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083107.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083107.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_083107.]: Solving cubic regression for CpG#6
Coefficients: 0Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.54266666666667Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 12.5
Root: 11.495
--> Root in between the borders! Added to results.
Coefficients: -15.692Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 25
Root: 26.346
--> Root in between the borders! Added to results.
Coefficients: -21.804Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 37.5
Root: 35.332
--> Root in between the borders! Added to results.
Coefficients: -33.2485Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 50
Root: 50.228
--> Root in between the borders! Added to results.
Coefficients: -46.704Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 62.5
Root: 65.055
--> Root in between the borders! Added to results.
Coefficients: -55.636Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 75
Root: 73.641
--> Root in between the borders! Added to results.
Coefficients: -71.3493333333333Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 87.5
Root: 86.903
--> Root in between the borders! Added to results.
Coefficients: -89.46Coefficients: 0.556476268933529Coefficients: 0.000806932475066736Coefficients: 2.57430483559797e-05
[20250402_083107.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083107.]: Solving cubic regression for CpG#7
Coefficients: 0Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.18066666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 12.5
Root: 8.108
--> Root in between the borders! Added to results.
Coefficients: -10.05Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 25
Root: 21.288
--> Root in between the borders! Added to results.
Coefficients: -16.236Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 37.5
Root: 36.173
--> Root in between the borders! Added to results.
Coefficients: -24.8165Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 50
Root: 54.247
--> Root in between the borders! Added to results.
Coefficients: -32.75Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 62.5
Root: 67.087
--> Root in between the borders! Added to results.
Coefficients: -39.954Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 75
Root: 76.377
--> Root in between the borders! Added to results.
Coefficients: -42.9206666666667Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 87.5
Root: 79.728
--> Root in between the borders! Added to results.
Coefficients: -66.008Coefficients: 0.55303217575518Coefficients: -0.00511940384404101Coefficients: 6.18988208648922e-05
[20250402_083107.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083107.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083107.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_083107.]: Solving cubic regression for CpG#9
Coefficients: 0Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 12.5
Root: 8.93
--> Root in between the borders! Added to results.
Coefficients: -13.716Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 25
Root: 24.492
--> Root in between the borders! Added to results.
Coefficients: -19.634Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 37.5
Root: 35.53
--> Root in between the borders! Added to results.
Coefficients: -30.406Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 50
Root: 52.349
--> Root in between the borders! Added to results.
Coefficients: -41.696Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 62.5
Root: 65.528
--> Root in between the borders! Added to results.
Coefficients: -51.9135Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 75
Root: 74.87
--> Root in between the borders! Added to results.
Coefficients: -64.5026666666667Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083107.]: Samplename: 87.5
Root: 84.256
--> Root in between the borders! Added to results.
Coefficients: -92Coefficients: 0.649873072577863Coefficients: -0.00573518801387237Coefficients: 8.43645728809374e-05
[20250402_083108.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083108.]: Solving cubic regression for row_means
Coefficients: 0Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -5.70125925925926Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 12.5
Root: 9.866
--> Root in between the borders! Added to results.
Coefficients: -14.126Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 25
Root: 24.413
--> Root in between the borders! Added to results.
Coefficients: -21.378Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 37.5
Root: 36.177
--> Root in between the borders! Added to results.
Coefficients: -31.7547777777778Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 50
Root: 51.091
--> Root in between the borders! Added to results.
Coefficients: -43.9506666666667Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 62.5
Root: 65.785
--> Root in between the borders! Added to results.
Coefficients: -53.632Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 75
Root: 75.683
--> Root in between the borders! Added to results.
Coefficients: -61.6601481481482Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 87.5
Root: 82.966
--> Root in between the borders! Added to results.
Coefficients: -83.9631111111111Coefficients: 0.586398180119032Coefficients: -0.00123897828816967Coefficients: 3.77130759809046e-05
[20250402_083108.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083108.]: ### Starting with regression calculations ###
[20250402_083108.]: Entered 'regression_type1'-Function
[20250402_083111.]: # CpG-site: CpG#1
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250402_083111.]: Logging df_agg: CpG#1
[20250402_083111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083111.]: c(0, 10.2789379687773, 21.5912618581737, 36.6165063803141, 52.7290217620987, 66.5324318982031, 75.7732681056135, 81.7721530184166, 100)
[20250402_083111.]: Entered 'hyperbolic_regression'-Function
[20250402_083111.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083112.]: Entered 'cubic_regression'-Function
[20250402_083112.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083113.]: # CpG-site: CpG#2
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250402_083113.]: Logging df_agg: CpG#2
[20250402_083113.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083113.]: c(0, 10.9910200331058, 26.4347343794858, 35.5445484590422, 48.1023951945168, 67.0857465067419, 75.4194602180407, 83.7851017057913, 100)
[20250402_083113.]: Entered 'hyperbolic_regression'-Function
[20250402_083113.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083115.]: Entered 'cubic_regression'-Function
[20250402_083115.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083116.]: # CpG-site: CpG#3
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250402_083116.]: Logging df_agg: CpG#3
[20250402_083116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083116.]: c(0, 9.38673392637229, 24.3726553415377, 36.1351252190462, 51.290483481273, 65.5610869969825, 77.682931580408, 80.3481110749784, 100)
[20250402_083116.]: Entered 'hyperbolic_regression'-Function
[20250402_083116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083117.]: Entered 'cubic_regression'-Function
[20250402_083117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083118.]: # CpG-site: CpG#4
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250402_083118.]: Logging df_agg: CpG#4
[20250402_083118.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083118.]: c(0, 11.333221967818, 22.9327025441323, 37.5415761160868, 48.7723103653381, 68.323814507742, 74.6144361781331, 82.8156863832731, 100)
[20250402_083118.]: Entered 'hyperbolic_regression'-Function
[20250402_083118.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083119.]: Entered 'cubic_regression'-Function
[20250402_083119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083120.]: # CpG-site: CpG#5
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250402_083120.]: Logging df_agg: CpG#5
[20250402_083120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083120.]: c(0, 9.59307352472009, 24.7039196286167, 38.0513608286781, 51.1867356506794, 62.26862037854, 75.7858670101849, 85.4752679494875, 100)
[20250402_083120.]: Entered 'hyperbolic_regression'-Function
[20250402_083120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083121.]: Entered 'cubic_regression'-Function
[20250402_083121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083112.]: # CpG-site: CpG#6
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250402_083113.]: Logging df_agg: CpG#6
[20250402_083113.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083113.]: c(0, 11.4954220530927, 26.3463219064414, 35.3317252573924, 50.227923198103, 65.0547254327623, 73.6409323113027, 86.9034526462823, 100)
[20250402_083113.]: Entered 'hyperbolic_regression'-Function
[20250402_083113.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083114.]: Entered 'cubic_regression'-Function
[20250402_083114.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083115.]: # CpG-site: CpG#7
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250402_083115.]: Logging df_agg: CpG#7
[20250402_083115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083115.]: c(0, 8.10849051770153, 21.2877667704468, 36.173114142988, 54.2470474820822, 67.0869477341973, 76.3774195175699, 79.7282731837602, 100)
[20250402_083115.]: Entered 'hyperbolic_regression'-Function
[20250402_083115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083116.]: Entered 'cubic_regression'-Function
[20250402_083116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083117.]: # CpG-site: CpG#8
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250402_083117.]: Logging df_agg: CpG#8
[20250402_083117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083117.]: c(0, 8.03884794173082, 26.0790124661259, 34.8640244910097, 52.3106100864949, 64.5844806617511, 77.5764831155946, 80.3258936673854, 100)
[20250402_083117.]: Entered 'hyperbolic_regression'-Function
[20250402_083117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083119.]: Entered 'cubic_regression'-Function
[20250402_083119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083120.]: # CpG-site: CpG#9
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250402_083120.]: Logging df_agg: CpG#9
[20250402_083120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083120.]: c(0, 8.92983720232018, 24.492281299778, 35.5300863746257, 52.3487602415591, 65.5277236843712, 74.8697077038883, 84.2557944227308, 100)
[20250402_083120.]: Entered 'hyperbolic_regression'-Function
[20250402_083120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083121.]: Entered 'cubic_regression'-Function
[20250402_083121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083122.]: # CpG-site: row_means
c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250402_083122.]: Logging df_agg: row_means
[20250402_083122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250402_083122.]: c(0, 9.86641397663336, 24.4129321171961, 36.1766819844577, 51.09059907333, 65.7845651788236, 75.6825697981982, 82.9660082109242, 100)
[20250402_083122.]: Entered 'hyperbolic_regression'-Function
[20250402_083122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083123.]: Entered 'cubic_regression'-Function
[20250402_083123.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental
[20250402_083128.]: Entered 'solving_equations'-Function
[20250402_083128.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 78.9856894800976
[20250402_083128.]: Samplename: Sample#1
Root: 78.986
--> Root in between the borders! Added to results.
Hyperbolic solved: 31.2695317984092
[20250402_083128.]: Samplename: Sample#10
Root: 31.27
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.7015782380441
[20250402_083128.]: Samplename: Sample#2
Root: 42.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.8152127901709
[20250402_083128.]: Samplename: Sample#3
Root: 57.815
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.2334360674289
[20250402_083128.]: Samplename: Sample#4
Root: 11.233
--> Root in between the borders! Added to results.
Hyperbolic solved: 23.5293831001518
[20250402_083128.]: Samplename: Sample#5
Root: 23.529
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7706743072545
[20250402_083128.]: Samplename: Sample#6
Root: 24.771
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.3953425213349
[20250402_083128.]: Samplename: Sample#7
Root: 46.395
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.45071436915
[20250402_083128.]: Samplename: Sample#8
Root: 84.451
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.41337105576252
[20250402_083128.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.413
--> '-10 < root < 0' --> substitute 0
[20250402_083128.]: Solving cubic regression for CpG#2
Coefficients: -59.7333333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#1
Root: 76.346
--> Root in between the borders! Added to results.
Coefficients: -19.048Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#10
Root: 31.371
--> Root in between the borders! Added to results.
Coefficients: -27.8783333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#2
Root: 43.142
--> Root in between the borders! Added to results.
Coefficients: -41.795Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#3
Root: 59.121
--> Root in between the borders! Added to results.
Coefficients: -2.21Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#4
Root: 4.128
--> Root in between the borders! Added to results.
Coefficients: -11.665Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#5
Root: 20.292
--> Root in between the borders! Added to results.
Coefficients: -10.08Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#6
Root: 17.745
--> Root in between the borders! Added to results.
Coefficients: -26.488Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#7
Root: 41.383
--> Root in between the borders! Added to results.
Coefficients: -70.532Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#8
Root: 85.378
--> Root in between the borders! Added to results.
Coefficients: -1.13Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083128.]: Samplename: Sample#9
Root: 2.127
--> Root in between the borders! Added to results.
[20250402_083128.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 74.5474014641742
[20250402_083128.]: Samplename: Sample#1
Root: 74.547
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.3579002775045
[20250402_083128.]: Samplename: Sample#10
Root: 28.358
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.6085496577593
[20250402_083128.]: Samplename: Sample#2
Root: 42.609
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.3286114696456
[20250402_083128.]: Samplename: Sample#3
Root: 56.329
--> Root in between the borders! Added to results.
Hyperbolic solved: 7.99034441243248
[20250402_083128.]: Samplename: Sample#4
Root: 7.99
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.7023143744962
[20250402_083128.]: Samplename: Sample#5
Root: 24.702
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.8868798900698
[20250402_083128.]: Samplename: Sample#6
Root: 26.887
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.8318233973603
[20250402_083128.]: Samplename: Sample#7
Root: 44.832
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6737871528405
[20250402_083128.]: Samplename: Sample#8
Root: 84.674
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.26200732612128
[20250402_083128.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.262
--> '-10 < root < 0' --> substitute 0
[20250402_083128.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 75.8433680333876
[20250402_083128.]: Samplename: Sample#1
Root: 75.843
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.0603248948201
[20250402_083128.]: Samplename: Sample#10
Root: 29.06
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.0355928114108
[20250402_083128.]: Samplename: Sample#2
Root: 44.036
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.7751115686327
[20250402_083128.]: Samplename: Sample#3
Root: 58.775
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.0319154866029
[20250402_083128.]: Samplename: Sample#4
Root: 11.032
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.9948971650737
[20250402_083128.]: Samplename: Sample#5
Root: 22.995
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.9415139419957
[20250402_083128.]: Samplename: Sample#6
Root: 27.942
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.4874049425657
[20250402_083128.]: Samplename: Sample#7
Root: 42.487
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6802730343613
[20250402_083128.]: Samplename: Sample#8
Root: 84.68
--> Root in between the borders! Added to results.
Hyperbolic solved: 3.00887785677921
[20250402_083128.]: Samplename: Sample#9
Root: 3.009
--> Root in between the borders! Added to results.
[20250402_083128.]: Solving cubic regression for CpG#5
Coefficients: -47.8373333333333Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083128.]: Samplename: Sample#1
Root: 72.291
--> Root in between the borders! Added to results.
Coefficients: -13.588Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#10
Root: 27.212
--> Root in between the borders! Added to results.
Coefficients: -25.3211428571429Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#2
Root: 44.85
--> Root in between the borders! Added to results.
Coefficients: -32.064Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#3
Root: 53.741
--> Root in between the borders! Added to results.
Coefficients: -4.074Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#4
Root: 9.444
--> Root in between the borders! Added to results.
Coefficients: -11.434Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#5
Root: 23.55
--> Root in between the borders! Added to results.
Coefficients: -13.294Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#6
Root: 26.722
--> Root in between the borders! Added to results.
Coefficients: -24.288Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#7
Root: 43.42
--> Root in between the borders! Added to results.
Coefficients: -63.134Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#8
Root: 88.215
--> Root in between the borders! Added to results.
Coefficients: 0.0360000000000005Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083129.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.091
--> '-10 < root < 0' --> substitute 0
[20250402_083129.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 79.2200555510382
[20250402_083129.]: Samplename: Sample#1
Root: 79.22
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.2526528381147
[20250402_083129.]: Samplename: Sample#10
Root: 30.253
--> Root in between the borders! Added to results.
Hyperbolic solved: 41.9196854329573
[20250402_083129.]: Samplename: Sample#2
Root: 41.92
--> Root in between the borders! Added to results.
Hyperbolic solved: 56.8984354098215
[20250402_083129.]: Samplename: Sample#3
Root: 56.898
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.81576403111374
[20250402_083129.]: Samplename: Sample#4
Root: 8.816
--> Root in between the borders! Added to results.
Hyperbolic solved: 18.6921622783918
[20250402_083129.]: Samplename: Sample#5
Root: 18.692
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.9815019073132
[20250402_083129.]: Samplename: Sample#6
Root: 29.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 42.8875178508205
[20250402_083129.]: Samplename: Sample#7
Root: 42.888
--> Root in between the borders! Added to results.
Hyperbolic solved: 86.6303733181195
[20250402_083129.]: Samplename: Sample#8
Root: 86.63
--> Root in between the borders! Added to results.
Hyperbolic solved: 1.38997712955107
[20250402_083129.]: Samplename: Sample#9
Root: 1.39
--> Root in between the borders! Added to results.
[20250402_083129.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 77.5278331978133
[20250402_083129.]: Samplename: Sample#1
Root: 77.528
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.0895401031897
[20250402_083129.]: Samplename: Sample#10
Root: 27.09
--> Root in between the borders! Added to results.
Hyperbolic solved: 48.4382794903846
[20250402_083129.]: Samplename: Sample#2
Root: 48.438
--> Root in between the borders! Added to results.
Hyperbolic solved: 58.8815971416453
[20250402_083129.]: Samplename: Sample#3
Root: 58.882
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.3295768294236
[20250402_083129.]: Samplename: Sample#4
Root: 13.33
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.9816196357542
[20250402_083129.]: Samplename: Sample#5
Root: 26.982
--> Root in between the borders! Added to results.
Hyperbolic solved: 30.9612159665911
[20250402_083129.]: Samplename: Sample#6
Root: 30.961
--> Root in between the borders! Added to results.
Hyperbolic solved: 45.7456547820365
[20250402_083129.]: Samplename: Sample#7
Root: 45.746
--> Root in between the borders! Added to results.
Hyperbolic solved: 84.6033538318025
[20250402_083129.]: Samplename: Sample#8
Root: 84.603
--> Root in between the borders! Added to results.
Hyperbolic solved: -2.87380061592101
[20250402_083130.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -2.874
--> '-10 < root < 0' --> substitute 0
[20250402_083130.]: Solving cubic regression for CpG#8
Coefficients: -55.3573333333333Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#1
Root: 72.421
--> Root in between the borders! Added to results.
Coefficients: -17.574Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#10
Root: 28.533
--> Root in between the borders! Added to results.
Coefficients: -22.9425714285714Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#2
Root: 35.766
--> Root in between the borders! Added to results.
Coefficients: -42.849Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#3
Root: 59.36
--> Root in between the borders! Added to results.
Coefficients: -4.604Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#4
Root: 8.481
--> Root in between the borders! Added to results.
Coefficients: -11.389Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#5
Root: 19.519
--> Root in between the borders! Added to results.
Coefficients: -25.784Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#6
Root: 39.413
--> Root in between the borders! Added to results.
Coefficients: -30.746Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#7
Root: 45.53
--> Root in between the borders! Added to results.
Coefficients: -66.912Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#8
Root: 83.654
--> Root in between the borders! Added to results.
Coefficients: 3.176Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083130.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -6.535
--> '-10 < root < 0' --> substitute 0
[20250402_083130.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 80.5486410672961
[20250402_083130.]: Samplename: Sample#1
Root: 80.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 27.810468482135
[20250402_083130.]: Samplename: Sample#10
Root: 27.81
--> Root in between the borders! Added to results.
Hyperbolic solved: 46.2641649294309
[20250402_083130.]: Samplename: Sample#2
Root: 46.264
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.1903653427228
[20250402_083130.]: Samplename: Sample#3
Root: 57.19
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.63886339746086
[20250402_083130.]: Samplename: Sample#4
Root: 8.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 24.2162393845509
[20250402_083130.]: Samplename: Sample#5
Root: 24.216
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.6394430638471
[20250402_083130.]: Samplename: Sample#6
Root: 39.639
--> Root in between the borders! Added to results.
Hyperbolic solved: 44.3080887012493
[20250402_083130.]: Samplename: Sample#7
Root: 44.308
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.3259098830063
[20250402_083130.]: Samplename: Sample#8
Root: 87.326
--> Root in between the borders! Added to results.
Hyperbolic solved: -1.17959639730045
[20250402_083130.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -1.18
--> '-10 < root < 0' --> substitute 0
[20250402_083130.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 76.7568961192102
[20250402_083130.]: Samplename: Sample#1
Root: 76.757
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.8326630603664
[20250402_083130.]: Samplename: Sample#10
Root: 28.833
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.0145327025204
[20250402_083130.]: Samplename: Sample#2
Root: 43.015
--> Root in between the borders! Added to results.
Hyperbolic solved: 57.6144798147902
[20250402_083130.]: Samplename: Sample#3
Root: 57.614
--> Root in between the borders! Added to results.
Hyperbolic solved: 8.86517972238162
[20250402_083130.]: Samplename: Sample#4
Root: 8.865
--> Root in between the borders! Added to results.
Hyperbolic solved: 22.1849817550475
[20250402_083130.]: Samplename: Sample#5
Root: 22.185
--> Root in between the borders! Added to results.
Hyperbolic solved: 29.1973843238972
[20250402_083130.]: Samplename: Sample#6
Root: 29.197
--> Root in between the borders! Added to results.
Hyperbolic solved: 43.9174258632975
[20250402_083130.]: Samplename: Sample#7
Root: 43.917
--> Root in between the borders! Added to results.
Hyperbolic solved: 85.6607695784409
[20250402_083130.]: Samplename: Sample#8
Root: 85.661
--> Root in between the borders! Added to results.
Hyperbolic solved: -0.551158207550385
[20250402_083130.]: Samplename: Sample#9
## WARNING ##
No fitting root within the borders found.
Negative numeric root found:
Root: -0.551
--> '-10 < root < 0' --> substitute 0
[20250402_083130.]: Entered 'solving_equations'-Function
[20250402_083130.]: Solving hyperbolic regression for CpG#1
Hyperbolic solved: 0
[20250402_083130.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 14.1381159662486
[20250402_083130.]: Samplename: 12.5
Root: 14.138
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1241053609707
[20250402_083130.]: Samplename: 25
Root: 26.124
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.3567419170867
[20250402_083130.]: Samplename: 37.5
Root: 39.357
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.9273107806133
[20250402_083130.]: Samplename: 50
Root: 52.927
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4010628999278
[20250402_083130.]: Samplename: 62.5
Root: 65.401
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.4183184249663
[20250402_083130.]: Samplename: 75
Root: 74.418
--> Root in between the borders! Added to results.
Hyperbolic solved: 80.5431520527512
[20250402_083130.]: Samplename: 87.5
Root: 80.543
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083130.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083130.]: Solving cubic regression for CpG#2
Coefficients: 0Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -6.05666666666666Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 12.5
Root: 10.991
--> Root in between the borders! Added to results.
Coefficients: -15.656Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 25
Root: 26.435
--> Root in between the borders! Added to results.
Coefficients: -22.054Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 37.5
Root: 35.545
--> Root in between the borders! Added to results.
Coefficients: -31.945Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 50
Root: 48.102
--> Root in between the borders! Added to results.
Coefficients: -49.68Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 62.5
Root: 67.086
--> Root in between the borders! Added to results.
Coefficients: -58.6825Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083130.]: Samplename: 75
Root: 75.419
--> Root in between the borders! Added to results.
Coefficients: -68.5533333333333Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083131.]: Samplename: 87.5
Root: 83.785
--> Root in between the borders! Added to results.
Coefficients: -90.294Coefficients: 0.526816753036989Coefficients: 0.00201323875068832Coefficients: 1.7479937189418e-05
[20250402_083131.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083131.]: Solving hyperbolic regression for CpG#3
Hyperbolic solved: 0
[20250402_083131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 10.8497990553835
[20250402_083131.]: Samplename: 12.5
Root: 10.85
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.1511183533449
[20250402_083131.]: Samplename: 25
Root: 26.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.2940213300522
[20250402_083131.]: Samplename: 37.5
Root: 37.294
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.419361136507
[20250402_083131.]: Samplename: 50
Root: 51.419
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.0212050873619
[20250402_083131.]: Samplename: 62.5
Root: 65.021
--> Root in between the borders! Added to results.
Hyperbolic solved: 76.9977789568509
[20250402_083131.]: Samplename: 75
Root: 76.998
--> Root in between the borders! Added to results.
Hyperbolic solved: 79.686036177122
[20250402_083131.]: Samplename: 87.5
Root: 79.686
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083131.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083131.]: Solving hyperbolic regression for CpG#4
Hyperbolic solved: 0
[20250402_083131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 13.2434477796981
[20250402_083131.]: Samplename: 12.5
Root: 13.243
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.0815867666892
[20250402_083131.]: Samplename: 25
Root: 25.082
--> Root in between the borders! Added to results.
Hyperbolic solved: 38.7956859187734
[20250402_083131.]: Samplename: 37.5
Root: 38.796
--> Root in between the borders! Added to results.
Hyperbolic solved: 49.1001600195185
[20250402_083131.]: Samplename: 50
Root: 49.1
--> Root in between the borders! Added to results.
Hyperbolic solved: 67.5620415214226
[20250402_083131.]: Samplename: 62.5
Root: 67.562
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.7554076043322
[20250402_083131.]: Samplename: 75
Root: 73.755
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.0327440839301
[20250402_083131.]: Samplename: 87.5
Root: 82.033
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083131.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083131.]: Solving cubic regression for CpG#5
Coefficients: 0Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.144Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 12.5
Root: 9.593
--> Root in between the borders! Added to results.
Coefficients: -12.102Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 25
Root: 24.704
--> Root in between the borders! Added to results.
Coefficients: -20.536Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 37.5
Root: 38.051
--> Root in between the borders! Added to results.
Coefficients: -30.0715Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 50
Root: 51.187
--> Root in between the borders! Added to results.
Coefficients: -39.034Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 62.5
Root: 62.269
--> Root in between the borders! Added to results.
Coefficients: -51.059Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 75
Root: 75.786
--> Root in between the borders! Added to results.
Coefficients: -60.3906666666667Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 87.5
Root: 85.475
--> Root in between the borders! Added to results.
Coefficients: -75.446Coefficients: 0.394384751236424Coefficients: 0.00395257736579547Coefficients: -3.51824878159709e-06
[20250402_083131.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_083131.]: Solving hyperbolic regression for CpG#6
Hyperbolic solved: 0
[20250402_083131.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.822687731114
[20250402_083131.]: Samplename: 12.5
Root: 11.823
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.5494368772504
[20250402_083131.]: Samplename: 25
Root: 26.549
--> Root in between the borders! Added to results.
Hyperbolic solved: 35.3846787677878
[20250402_083131.]: Samplename: 37.5
Root: 35.385
--> Root in between the borders! Added to results.
Hyperbolic solved: 50.1264563333089
[20250402_083131.]: Samplename: 50
Root: 50.126
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.9875101866844
[20250402_083131.]: Samplename: 62.5
Root: 64.988
--> Root in between the borders! Added to results.
Hyperbolic solved: 73.6494948240195
[20250402_083131.]: Samplename: 75
Root: 73.649
--> Root in between the borders! Added to results.
Hyperbolic solved: 87.0033714659226
[20250402_083131.]: Samplename: 87.5
Root: 87.003
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083131.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083132.]: Solving hyperbolic regression for CpG#7
Hyperbolic solved: 0
[20250402_083132.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.7925453863418
[20250402_083132.]: Samplename: 12.5
Root: 11.793
--> Root in between the borders! Added to results.
Hyperbolic solved: 26.2042827174053
[20250402_083132.]: Samplename: 25
Root: 26.204
--> Root in between the borders! Added to results.
Hyperbolic solved: 39.2081609373531
[20250402_083132.]: Samplename: 37.5
Root: 39.208
--> Root in between the borders! Added to results.
Hyperbolic solved: 54.3620766326312
[20250402_083132.]: Samplename: 50
Root: 54.362
--> Root in between the borders! Added to results.
Hyperbolic solved: 66.0664882334621
[20250402_083132.]: Samplename: 62.5
Root: 66.066
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.1981507250883
[20250402_083132.]: Samplename: 75
Root: 75.198
--> Root in between the borders! Added to results.
Hyperbolic solved: 78.6124357632637
[20250402_083132.]: Samplename: 87.5
Root: 78.612
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083132.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083132.]: Solving cubic regression for CpG#8
Coefficients: 0Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Coefficients: -4.35066666666667Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 12.5
Root: 8.039
--> Root in between the borders! Added to results.
Coefficients: -15.834Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 25
Root: 26.079
--> Root in between the borders! Added to results.
Coefficients: -22.254Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 37.5
Root: 34.864
--> Root in between the borders! Added to results.
Coefficients: -36.529Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 50
Root: 52.311
--> Root in between the borders! Added to results.
Coefficients: -47.73Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 62.5
Root: 64.584
--> Root in between the borders! Added to results.
Coefficients: -60.5715Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 75
Root: 77.576
--> Root in between the borders! Added to results.
Coefficients: -63.414Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 87.5
Root: 80.326
--> Root in between the borders! Added to results.
Coefficients: -84.964Coefficients: 0.51096176300103Coefficients: 0.00379498058303308Coefficients: -4.08198213043375e-06
[20250402_083132.]: Samplename: 100
## WARNING ##
No fitting root within the borders found.
Positive numeric root found:
Root: 100
--> '100 < root < 110' --> substitute 100
[20250402_083132.]: Solving hyperbolic regression for CpG#9
Hyperbolic solved: 0
[20250402_083132.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 12.2094906593745
[20250402_083132.]: Samplename: 12.5
Root: 12.209
--> Root in between the borders! Added to results.
Hyperbolic solved: 28.0738986154201
[20250402_083132.]: Samplename: 25
Root: 28.074
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.6720254587223
[20250402_083132.]: Samplename: 37.5
Root: 37.672
--> Root in between the borders! Added to results.
Hyperbolic solved: 52.3746308870569
[20250402_083132.]: Samplename: 50
Root: 52.375
--> Root in between the borders! Added to results.
Hyperbolic solved: 64.8693631845077
[20250402_083132.]: Samplename: 62.5
Root: 64.869
--> Root in between the borders! Added to results.
Hyperbolic solved: 74.2598902601534
[20250402_083132.]: Samplename: 75
Root: 74.26
--> Root in between the borders! Added to results.
Hyperbolic solved: 83.9376844048195
[20250402_083132.]: Samplename: 87.5
Root: 83.938
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083132.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
[20250402_083132.]: Solving hyperbolic regression for row_means
Hyperbolic solved: 0
[20250402_083132.]: Samplename: 0
Root: 0
--> Root in between the borders! Added to results.
Hyperbolic solved: 11.1506882890389
[20250402_083132.]: Samplename: 12.5
Root: 11.151
--> Root in between the borders! Added to results.
Hyperbolic solved: 25.841636381907
[20250402_083132.]: Samplename: 25
Root: 25.842
--> Root in between the borders! Added to results.
Hyperbolic solved: 37.0462679509085
[20250402_083132.]: Samplename: 37.5
Root: 37.046
--> Root in between the borders! Added to results.
Hyperbolic solved: 51.1681297765954
[20250402_083132.]: Samplename: 50
Root: 51.168
--> Root in between the borders! Added to results.
Hyperbolic solved: 65.4258217891781
[20250402_083132.]: Samplename: 62.5
Root: 65.426
--> Root in between the borders! Added to results.
Hyperbolic solved: 75.285632789037
[20250402_083132.]: Samplename: 75
Root: 75.286
--> Root in between the borders! Added to results.
Hyperbolic solved: 82.6475419323379
[20250402_083132.]: Samplename: 87.5
Root: 82.648
--> Root in between the borders! Added to results.
Hyperbolic solved: 100
[20250402_083132.]: Samplename: 100
Root: 100
--> Root in between the borders! Added to results.
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
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singular gradient
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singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
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Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[20250402_083539.]: Entered 'clean_dt'-Function
[20250402_083539.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_083539.]: got experimental data
[20250402_083539.]: Entered 'clean_dt'-Function
[20250402_083539.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data)
[20250402_083539.]: got experimental data
[20250402_083540.]: Entered 'clean_dt'-Function
[20250402_083540.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_083540.]: got calibration data
[20250402_083540.]: Entered 'clean_dt'-Function
[20250402_083540.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data)
[20250402_083540.]: got calibration data
[20250402_083540.]: Entered 'hyperbolic_regression'-Function
[20250402_083540.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
Error in (function (formula, data = parent.frame(), start, control = nls.control(), :
singular gradient
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• On CRAN (4): 'test-algorithm_minmax_FALSE.R:80:5',
'test-algorithm_minmax_TRUE.R:76:5', 'test-hyperbolic.R:27:5',
'test-lints.R:12:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-algorithm_minmax_FALSE_re.R:170:5'): algorithm test, type 1, minmax = FALSE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_FALSE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-algorithm_minmax_TRUE_re.R:170:5'): algorithm test, type 1, minmax = TRUE selection_method = RelError ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-algorithm_minmax_TRUE_re.R:170:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:17:5'): test normal function of file import of type 1 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:17:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-clean_dt.R:65:5'): test normal function of file import of type 2 ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-clean_dt.R:65:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
── Error ('test-create_aggregated.R:19:5'): test functioning of aggregated function ──
Error in `structure(.Data = base::quote(NULL))`: attempt to set an attribute on NULL
Backtrace:
▆
1. └─testthat::expect_snapshot_value(...) at test-create_aggregated.R:19:5
2. ├─testthat:::check_roundtrip(...)
3. │ └─testthat:::waldo_compare(...)
4. │ └─waldo::compare(x, y, ..., x_arg = x_arg, y_arg = y_arg)
5. │ └─waldo:::compare_structure(x, y, paths = c(x_arg, y_arg), opts = opts)
6. │ └─rlang::is_missing(y)
7. └─testthat (local) load(save(x))
8. └─jsonlite::unserializeJSON(x)
9. └─jsonlite:::unpack(parseJSON(txt))
10. └─base::lapply(obj$attributes, unpack)
11. └─jsonlite (local) FUN(X[[i]], ...)
12. ├─base::do.call("structure", newdata, quote = TRUE)
13. └─base::structure(.Data = base::quote(NULL))
[ FAIL 5 | WARN 51 | SKIP 4 | PASS 51 ]
Error: Test failures
Execution halted
Error in deferred_run(env) : could not find function "deferred_run"
Calls: <Anonymous>
Flavor: r-devel-linux-x86_64-fedora-gcc