Function dm2 conducts Diallel Method 2 analysis for RCBD and Alpha Lattice design.
Example 1: Diallel Method 2 analysis for RCBD design.
# Load the package
library(gpbStat)
#Load the dataset
data(dm2rcbd)
# View the structure of dataframe.
str(dm2rcbd)
#> Classes 'tbl_df', 'tbl' and 'data.frame': 240 obs. of 4 variables:
#> $ rep : chr "R1" "R1" "R1" "R1" ...
#> $ parent1: num 1 1 1 1 1 1 1 1 1 1 ...
#> $ parent2: num 1 2 3 4 5 6 7 8 9 10 ...
#> $ DTP : num 66.1 58.5 64.6 64.2 59.3 ...
# Conduct Line x Tester analysis
result = dm2(dm2rcbd, rep, parent1, parent2, DTP)
# View the output
result
#> $Means
#> Parent2
#> Parent1 1 2 3 4 5 6 7 8
#> 1 65.31769 56.88736 62.87728 62.19819 57.24611 61.77934 60.23199 59.45378
#> 2 56.88736 63.30941 62.59786 59.43587 56.45149 57.55432 54.75840 56.11425
#> 3 62.87728 62.59786 58.36095 58.25634 60.71883 55.22639 55.12505 54.39954
#> 4 62.19819 59.43587 58.25634 63.77961 57.15805 63.32091 62.43797 55.12414
#> 5 57.24611 56.45149 60.71883 57.15805 65.04595 55.25778 63.89851 59.23282
#> 6 61.77934 57.55432 55.22639 63.32091 55.25778 56.88325 57.10090 58.62343
#> 7 60.23199 54.75840 55.12505 62.43797 63.89851 57.10090 62.30133 58.96640
#> 8 59.45378 56.11425 54.39954 55.12414 59.23282 58.62343 58.96640 58.63107
#> 9 59.99343 57.79246 54.50506 54.49473 54.72035 57.53263 62.70356 63.63800
#> 10 59.99797 58.12088 59.31957 60.58825 61.87032 61.72040 62.36893 62.04768
#> 11 59.14957 58.54128 64.32069 57.06435 62.42775 62.55616 59.98320 57.74982
#> 12 61.39705 62.77292 56.14993 55.74474 59.85430 58.16224 55.39976 62.39437
#> 13 60.53815 61.92330 58.71091 58.35329 58.69939 63.75573 62.00640 62.53515
#> 14 58.17887 59.62638 60.77991 56.50338 58.24740 60.36659 54.76567 55.49799
#> 15 60.59376 62.45391 56.91712 54.69967 56.86290 57.49282 57.68658 58.62889
#> Parent2
#> Parent1 9 10 11 12 13 14 15
#> 1 59.99343 59.99797 59.14957 61.39705 60.53815 58.17887 60.59376
#> 2 57.79246 58.12088 58.54128 62.77292 61.92330 59.62638 62.45391
#> 3 54.50506 59.31957 64.32069 56.14993 58.71091 60.77991 56.91712
#> 4 54.49473 60.58825 57.06435 55.74474 58.35329 56.50338 54.69967
#> 5 54.72035 61.87032 62.42775 59.85430 58.69939 58.24740 56.86290
#> 6 57.53263 61.72040 62.55616 58.16224 63.75573 60.36659 57.49282
#> 7 62.70356 62.36893 59.98320 55.39976 62.00640 54.76567 57.68658
#> 8 63.63800 62.04768 57.74982 62.39437 62.53515 55.49799 58.62889
#> 9 59.77470 62.65702 60.58196 62.10251 59.52592 58.40839 55.08259
#> 10 62.65702 63.39327 62.95963 58.22418 57.78493 57.43079 59.65661
#> 11 60.58196 62.95963 64.44419 62.62254 62.53704 64.48712 62.86311
#> 12 62.10251 58.22418 62.62254 64.76169 60.89386 59.95167 62.43614
#> 13 59.52592 57.78493 62.53704 60.89386 61.82842 55.83338 63.19762
#> 14 58.40839 57.43079 64.48712 59.95167 55.83338 63.09888 55.26263
#> 15 55.08259 59.65661 62.86311 62.43614 63.19762 55.26263 58.64814
#>
#> $ANOVA
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 1 503.76789 503.7678929 979.33212 2.878133e-59
#> Genotypes 119 2050.09712 17.2277069 33.49091 1.434100e-58
#> Residuals 119 61.21353 0.5143994 NA NA
#>
#> $`Co efficient of ariation`
#> [1] 1.202472
#>
#> $`Diallel ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> gca 14 190.03 13.5738 52.775 < 2.2e-16 ***
#> sca 105 835.02 7.9525 30.920 < 2.2e-16 ***
#> Error 119 30.61 0.2572
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> $`Genetic variances`
#> Components
#> gca 0.7833266
#> sca 7.6953340
#> gca/sca 0.1017924
#>
#> $`Combining ability effects`
#> Parent1 Parent2 Parent3 Parent4 Parent5 Parent6
#> Parent1 0.9903421 -3.5909078 3.282732 2.2323001 -3.29618002 1.738335
#> Parent2 NA -0.1572282 4.150876 0.6175547 -2.94322206 -1.339114
#> Parent3 NA NA -1.040942 0.3217366 2.20783036 -2.783330
#> Parent4 NA NA NA -0.6696061 -1.72428674 4.939854
#> Parent5 NA NA NA NA -0.09320439 -3.699678
#> Parent6 NA NA NA NA NA -0.594485
#> Parent7 NA NA NA NA NA NA
#> Parent8 NA NA NA NA NA NA
#> Parent9 NA NA NA NA NA NA
#> Parent10 NA NA NA NA NA NA
#> Parent11 NA NA NA NA NA NA
#> Parent12 NA NA NA NA NA NA
#> Parent13 NA NA NA NA NA NA
#> Parent14 NA NA NA NA NA NA
#> Parent15 NA NA NA NA NA NA
#> Parent7 Parent8 Parent9 Parent10 Parent11 Parent12
#> Parent1 -0.2690067 -0.4373583 0.007025405 -1.6499496 -3.3924020 -0.02122193
#> Parent2 -4.5950278 -2.6293207 -1.046375687 -2.3794715 -2.8531208 2.50222266
#> Parent3 -3.3446630 -3.4603089 -3.450064238 -0.2970702 3.8099996 -3.23705191
#> Parent4 3.5969203 -3.1070514 -3.831733007 0.6002765 -3.8176735 -4.01358089
#> Parent5 4.4810596 0.4252294 -4.182515333 1.3059498 0.9693226 -0.48041658
#> Parent6 -1.8152715 0.3171145 -0.868947362 1.6573031 1.5990125 -1.67119915
#> Parent7 -0.1344958 0.2001023 3.841983864 1.8458473 -1.4339331 -4.89366893
#> Parent8 NA -0.7443537 5.386282248 2.1344515 -3.0574576 2.71080035
#> Parent9 NA NA -0.649082319 2.6485273 -0.3205932 2.32366496
#> Parent10 NA NA NA 1.0124292 0.3955646 -3.21617158
#> Parent11 NA NA NA NA 1.9064819 0.28813378
#> Parent12 NA NA NA NA NA 0.78277604
#> Parent13 NA NA NA NA NA NA
#> Parent14 NA NA NA NA NA NA
#> Parent15 NA NA NA NA NA NA
#> Parent13 Parent14 Parent15
#> Parent1 -1.01672572 -1.7045958 0.7342505
#> Parent2 1.51599938 0.8904886 3.7419789
#> Parent3 -0.81268115 2.9277286 -0.9111020
#> Parent4 -1.54164192 -1.7201357 -3.4998837
#> Parent5 -1.77193727 -0.5525174 -1.9130559
#> Parent6 3.78568415 2.0679497 -0.7818603
#> Parent7 1.57636353 -3.9929557 -1.0480845
#> Parent8 2.71496708 -2.6507822 0.5040763
#> Parent9 -0.38953293 0.1643507 -3.1374949
#> Parent10 -3.79202953 -2.4747643 -0.2249810
#> Parent11 0.06602831 3.6875123 2.0874612
#> Parent12 -0.45345331 0.2757705 2.7841964
#> Parent13 0.91938373 -3.9791285 3.4090766
#> Parent14 NA -0.7520286 -2.8545007
#> Parent15 NA NA -0.7759868
#>
#> $`Standard Error`
#> SE.gi SE.sii SE.sij SE.gi.gj SE.sii.sjj SE.sij.sik SE.sij.skl
#> 0.1188308 0.4783640 0.4456157 0.1739505 0.6271876 0.6958022 0.6737075
#>
#> $`Critical Diffiernece`
#> CD.gi CD.sii CD.sij CD.gi.gj CD.sii.sjj CD.sij.sik CD.sij.skl
#> 0.2352969 0.9472085 0.8823635 0.3444394 1.2418941 1.3777578 1.3340082Example 2: Diallel Method 2 analysis for Alpha Lattice design.
# Load the package
library(gpbStat)
#Load the dataset
data(dm2alpha)
# View the structure of dataframe.
str(dm2alpha)
#> 'data.frame': 240 obs. of 5 variables:
#> $ replication: chr "r1" "r1" "r1" "r1" ...
#> $ block : chr "b2" "b5" "b6" "b12" ...
#> $ parent1 : int 1 1 2 1 2 3 1 2 3 4 ...
#> $ parent2 : int 1 2 2 3 3 3 4 4 4 4 ...
#> $ TW : num 27.7 27.7 44.6 44.6 34.1 ...
# Conduct Diallel Analysis
result1 = dm2(dm2alpha, replication, parent1, parent2, TW, block)
# View the output
result1
#> $Means
#> Parent2
#> Parent1 1 2 3 4 5 6 7 8
#> 1 34.43711 34.43711 39.37157 33.06467 37.36522 34.53972 39.23268 41.75649
#> 2 34.43711 39.37157 38.69548 33.06467 37.36522 35.71489 36.45700 41.75649
#> 3 39.37157 38.69548 38.69548 27.84591 33.95838 35.71489 36.45700 32.21106
#> 4 33.06467 33.06467 27.84591 27.84591 33.95838 34.76627 37.79556 32.21106
#> 5 37.36522 37.36522 33.95838 33.95838 34.53972 34.76627 37.79556 36.55245
#> 6 34.53972 35.71489 35.71489 34.76627 34.76627 39.23268 32.30785 36.55245
#> 7 39.23268 36.45700 36.45700 37.79556 37.79556 32.30785 32.30785 29.98739
#> 8 41.75649 41.75649 32.21106 32.21106 36.55245 36.55245 29.98739 29.98739
#> 9 35.18432 35.18432 40.37017 40.37017 37.10259 37.10259 41.15481 41.15481
#> 10 37.65202 36.53618 36.53618 39.03769 39.03769 34.00577 34.00577 29.19375
#> 11 35.24205 31.97488 31.97488 32.49791 32.49791 42.04574 42.04574 28.60725
#> 12 37.11366 37.11366 34.85844 34.85844 35.99001 35.99001 32.81075 32.81075
#> 13 30.38480 30.38480 40.39449 40.39449 42.50756 42.50756 33.31475 33.31475
#> 14 32.96673 36.88250 36.88250 40.06831 40.06831 34.73526 34.73526 36.98097
#> 15 36.19758 34.07647 34.07647 35.96782 35.96782 34.17183 34.17183 33.36153
#> Parent2
#> Parent1 9 10 11 12 13 14 15
#> 1 35.18432 37.65202 35.24205 37.11366 30.38480 32.96673 36.19758
#> 2 35.18432 36.53618 31.97488 37.11366 30.38480 36.88250 34.07647
#> 3 40.37017 36.53618 31.97488 34.85844 40.39449 36.88250 34.07647
#> 4 40.37017 39.03769 32.49791 34.85844 40.39449 40.06831 35.96782
#> 5 37.10259 39.03769 32.49791 35.99001 42.50756 40.06831 35.96782
#> 6 37.10259 34.00577 42.04574 35.99001 42.50756 34.73526 34.17183
#> 7 41.15481 34.00577 42.04574 32.81075 33.31475 34.73526 34.17183
#> 8 41.15481 29.19375 28.60725 32.81075 33.31475 36.98097 33.36153
#> 9 37.65202 29.19375 28.60725 28.81494 36.98787 36.98097 33.36153
#> 10 29.19375 35.24205 34.70764 28.81494 36.98787 34.43934 40.02420
#> 11 28.60725 34.70764 34.70764 29.38373 32.50283 34.43934 40.02420
#> 12 28.81494 28.81494 29.38373 29.38373 32.50283 29.15149 37.41538
#> 13 36.98787 36.98787 32.50283 32.50283 32.96673 29.15149 37.41538
#> 14 36.98097 34.43934 34.43934 29.15149 29.15149 36.19758 30.95810
#> 15 33.36153 40.02420 40.02420 37.41538 37.41538 30.95810 30.95810
#>
#> $ANOVA
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 1 81.1883 81.18830 2.3477738 0.1287171
#> Treatments 119 3086.8726 25.94011 0.7501265 0.9324158
#> Replication:Block 22 774.0140 35.18245 1.0173933 0.4515091
#> Residuals 97 3354.3545 34.58097 NA NA
#>
#> $`Co efficient of Variation`
#> [1] 16.69164
#>
#> $`Diallel ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> gca 14 214.93 15.352 0.8879 0.5740
#> sca 105 1328.50 12.652 0.7318 0.9414
#> Error 97 1677.18 17.291
#>
#> $`Genetic variances`
#> Components
#> gca -0.11400667
#> sca -4.63807702
#> gca/sca 0.02458059
#>
#> $`Combining ability effects`
#> Parent1 Parent2 Parent3 Parent4 Parent5 Parent6
#> Parent1 0.5702231 -2.2282544 2.8031705 -2.0245991 0.36891376 -2.4197435
#> Parent2 NA 0.8645744 1.8327300 -2.3189504 0.07456246 -1.5389250
#> Parent3 NA NA 0.7676086 -7.4407375 -3.23531254 -1.4419592
#> Parent4 NA NA NA -0.7115204 -1.75618353 -0.9114479
#> Parent5 NA NA NA NA 1.19551556 -2.8184839
#> Parent6 NA NA NA NA NA 1.1586731
#> Parent7 NA NA NA NA NA NA
#> Parent8 NA NA NA NA NA NA
#> Parent9 NA NA NA NA NA NA
#> Parent10 NA NA NA NA NA NA
#> Parent11 NA NA NA NA NA NA
#> Parent12 NA NA NA NA NA NA
#> Parent13 NA NA NA NA NA NA
#> Parent14 NA NA NA NA NA NA
#> Parent15 NA NA NA NA NA NA
#> Parent7 Parent8 Parent9 Parent10 Parent11 Parent12
#> Parent1 3.2437392 6.971173 -1.39206575 2.0295979 0.4837521 3.5065499
#> Parent2 0.1737017 6.676821 -1.68641706 0.6194025 -3.0777614 3.2121986
#> Parent3 0.2706675 -2.771642 3.59640003 0.7163682 -2.9807956 1.0539489
#> Parent4 3.0883589 -1.292513 5.07552903 4.6970040 -0.9786428 2.5330779
#> Parent5 1.1813229 1.141844 -0.09908242 2.7899680 -2.8856788 1.7576095
#> Parent6 -4.2695400 1.178686 -0.06223995 -2.2051039 6.6989990 1.7944520
#> Parent7 0.1881555 -4.415855 4.96049545 -1.2345864 7.6695165 -0.4142896
#> Parent8 NA -1.015472 6.16412289 -4.8429771 -4.5653485 0.7893378
#> Parent9 NA NA 0.77559560 -6.6340446 -6.3564160 -4.9975405
#> Parent10 NA NA NA -0.1783598 0.6979297 -4.0435851
#> Parent11 NA NA NA NA -1.0424929 -2.6106636
#> Parent12 NA NA NA NA NA -2.1936764
#> Parent13 NA NA NA NA NA NA
#> Parent14 NA NA NA NA NA NA
#> Parent15 NA NA NA NA NA NA
#> Parent13 Parent14 Parent15
#> Parent1 -5.47456663 -2.6662196 0.6663589
#> Parent2 -5.76891793 0.9552010 -1.7490983
#> Parent3 4.33773631 1.0521668 -1.6521325
#> Parent4 5.81686532 5.7171100 1.7183466
#> Parent5 6.02289319 3.8100741 -0.1886894
#> Parent6 6.05973565 -1.4861358 -1.9478368
#> Parent7 -2.16254868 -0.5156182 -0.9773192
#> Parent8 -0.95892124 2.9337212 -0.5839923
#> Parent9 0.92312510 1.1426537 -2.3750598
#> Parent10 1.87708054 -0.4450236 5.2415658
#> Parent11 -1.74382435 0.4191094 6.1056988
#> Parent12 -0.59264080 -3.7175549 4.6480600
#> Parent13 0.05858292 -5.9698143 2.3958007
#> Parent14 NA -0.1678390 -3.8350536
#> Parent15 NA NA -0.2695682
#>
#> $`Standard Error`
#> SE.gi SE.sii SE.sij SE.gi.gj SE.sii.sjj SE.sij.sik SE.sij.skl
#> 0.9743109 3.9221739 3.6536657 1.4262451 5.1423997 5.7049802 5.5238233
#>
#> $`Critical Diffiernece`
#> CD.gi CD.sii CD.sij CD.gi.gj CD.sii.sjj CD.sij.sik CD.sij.skl
#> 1.933737 7.784429 7.251515 2.830702 10.206240 11.322806 10.963260