Last updated on 2025-12-06 00:48:51 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.3-4 | 2.42 | 28.04 | 30.46 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.3-4 | 1.84 | 15.55 | 17.39 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.3-4 | 46.70 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.3-4 | 43.73 | OK | |||
| r-devel-windows-x86_64 | 0.3-4 | 4.00 | 46.00 | 50.00 | OK | |
| r-patched-linux-x86_64 | 0.3-4 | 2.17 | 25.44 | 27.61 | OK | |
| r-release-linux-x86_64 | 0.3-4 | 2.20 | 26.52 | 28.72 | OK | |
| r-release-macos-arm64 | 0.3-4 | OK | ||||
| r-release-macos-x86_64 | 0.3-4 | 3.00 | 32.00 | 35.00 | OK | |
| r-release-windows-x86_64 | 0.3-4 | 4.00 | 45.00 | 49.00 | OK | |
| r-oldrel-macos-arm64 | 0.3-4 | OK | ||||
| r-oldrel-macos-x86_64 | 0.3-4 | 4.00 | 60.00 | 64.00 | OK | |
| r-oldrel-windows-x86_64 | 0.3-4 | 4.00 | 112.00 | 116.00 | OK |
Version: 0.3-4
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘G. Grothendieck <ggrothendieck@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "G.",
family = "Grothendieck",
role = c("aut", "cre"),
email = "ggrothendieck@gmail.com"),
person(given = c("R", "Core"),
family = "Team",
role = "aut",
comment = "nls"))
as necessary.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.3-4
Check: examples
Result: ERROR
Running examples in ‘nls2-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: nls2
> ### Title: Nonlinear Least Squares with Brute Force
> ### Aliases: nls2
> ### Keywords: nonlinear regression models
>
> ### ** Examples
>
>
> y <- c(44,36,31,39,38,26,37,33,34,48,25,22,44,5,9,13,17,15,21,10,16,22,
+ 13,20,9,15,14,21,23,23,32,29,20,26,31,4,20,25,24,32,23,33,34,23,28,30,10,29,
+ 40,10,8,12,13,14,56,47,44,37,27,17,32,31,26,23,31,34,37,32,26,37,28,38,35,27,
+ 34,35,32,27,22,23,13,28,13,22,45,33,46,37,21,28,38,21,18,21,18,24,18,23,22,
+ 38,40,52,31,38,15,21)
>
> x <- c(26.22,20.45,128.68,117.24,19.61,295.21,31.83,30.36,13.57,60.47,
+ 205.30,40.21,7.99,1.18,5.40,13.37,4.51,36.61,7.56,10.30,7.29,9.54,6.93,12.60,
+ 2.43,18.89,15.03,14.49,28.46,36.03,38.52,45.16,58.27,67.13,92.33,1.17,
+ 29.52,84.38,87.57,109.08,72.28,66.15,142.27,76.41,105.76,73.47,1.71,305.75,
+ 325.78,3.71,6.48,19.26,3.69,6.27,1689.67,95.23,13.47,8.60,96.00,436.97,
+ 472.78,441.01,467.24,1169.11,1309.10,1905.16,135.92,438.25,526.68,88.88,31.43,
+ 21.22,640.88,14.09,28.91,103.38,178.99,120.76,161.15,137.38,158.31,179.36,
+ 214.36,187.05,140.92,258.42,85.86,47.70,44.09,18.04,127.84,1694.32,34.27,
+ 75.19,54.39,79.88,63.84,82.24,88.23,202.66,148.93,641.76,20.45,145.31,
+ 27.52,30.70)
>
> ## Example 1
> ## brute force followed by nls optimization
>
> fo <- y ~ Const + B * (x ^ A)
>
> # pass our own set of starting values
> # returning result of brute force search as nls object
> st1 <- expand.grid(Const = seq(-100, 100, len = 4),
+ B = seq(-100, 100, len = 4), A = seq(-1, 1, len = 4))
> mod1 <- nls2(fo, start = st1, algorithm = "brute-force")
> mod1
Nonlinear regression model
model: y ~ Const + B * (x^A)
data: parent.frame()
Const B A
33.3333 -33.3333 -0.3333
residual sum-of-squares: 10078
Number of iterations to convergence: 64
Achieved convergence tolerance: NA
> # use nls object mod1 just calculated as starting value for
> # nls optimization. Same as: nls(fo, start = coef(mod1))
> nls2(fo, start = mod1)
Nonlinear regression model
model: y ~ Const + B * (x^A)
data: parent.frame()
Const B A
33.9291 -33.4595 -0.4464
residual sum-of-squares: 8751
Number of iterations to convergence: 3
Achieved convergence tolerance: 3.025e-06
>
> ## Example 2
>
> # pass a 2-row data frame and let nls2 calculate grid
> st2 <- data.frame(Const = c(-100, 100), B = c(-100, 100), A = c(-1, 1))
> mod2 <- nls2(fo, start = st2, algorithm = "brute-force")
> mod2
Nonlinear regression model
model: y ~ Const + B * (x^A)
data: parent.frame()
Const B A
33.3333 -33.3333 -0.3333
residual sum-of-squares: 10078
Number of iterations to convergence: 64
Achieved convergence tolerance: NA
> # use nls object mod1 just calculated as starting value for
> # nls optimization. Same as: nls(fo, start = coef(mod2))
> nls2(fo, start = mod2)
Nonlinear regression model
model: y ~ Const + B * (x^A)
data: parent.frame()
Const B A
33.9291 -33.4595 -0.4464
residual sum-of-squares: 8751
Number of iterations to convergence: 3
Achieved convergence tolerance: 3.025e-06
>
> ## Example 3
>
> # Create same starting values as in Example 2
> # running an nls optimization from each one and picking best.
> # This one does an nls optimization for every random point
> # generated whereas Example 2 only does a single nls optimization
> nls2(fo, start = st2, control = nls.control(warnOnly = TRUE))
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
number of iterations exceeded maximum of 50
Warning in (function (formula, data = parent.frame(), start, control = nls.control(), :
step factor 0.000488281 reduced below 'minFactor' of 0.000976562
Nonlinear regression model
model: y ~ Const + B * (x^A)
data: parent.frame()
Const B A
33.9290 -33.4595 -0.4464
residual sum-of-squares: 8751
Number of iterations to convergence: 4
Achieved convergence tolerance: 4.765e-07
>
> ## Example 4
>
> # Investigate singular jacobian at the start value
> # Note that this cannot be done with nls since the singular jacobian at
> # the initial conditions would stop it with an error.
>
> DF1 <- data.frame(y=1:9, one=rep(1,9))
> xx <- nls2(y~(a+2*b)*one, DF1, start = c(a=1, b=1), algorithm = "brute-force")
> svd(xx$m$Rmat())[-2]
$d
[1] 6.708204 0.000000
$v
[,1] [,2]
[1,] -0.4472136 -0.8944272
[2,] -0.8944272 0.4472136
>
> ## Example 5
>
> # plinear-lhs example
> # Thanks to John Nash for suggesting this truncation of the
> # Ratkowsky2 dataset. Full dataset: data(Ratkowsky2, package = "NISTnls")
> # Use plinear-lhs to get starting values and then run nls via nls2 for
> # final answer.
>
> pastured <- data.frame(
+ time=c(9, 14, 21, 28, 42, 57, 63, 70, 79),
+ yield= c(8.93, 10.8, 18.59, 22.33, 39.35, 56.11, 61.73, 64.62, 67.08))
> fo <- yield ~ cbind(1, - exp(-exp(t3+t4*log(time))))
>
> gstart <- data.frame(t3 = c(-10, 10), t4 = c(1, 8))
> set.seed(123)
> junk <- capture.output(fm0 <- nls2(fo, data = pastured, start = gstart, alg = "plinear-lhs",
+ control = nls.control(maxiter = 1000)), type = "message")
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc