The goal of selection.index is to easily construct the selection index and based on the these indices select the plant traits for the overall improvement of the plant.
You can install the released version of selection.index from CRAN with:
install.packages("selection.index")from github with:
devtools::install_github("zankrut20/selection.index")This is a basic example which shows you how to solve a common problem:
Dataset seldata is included in package.
library(selection.index)
head(seldata)
#> rep treat sypp dtf rpp ppr ppp spp pw
#> 1 1 G1 5.4306 42.5000 2.8333 2.0085 7.5833 2.7020 0.5523
#> 2 2 G1 5.4583 42.5000 3.2000 3.7179 7.8000 2.5152 0.7431
#> 3 3 G1 5.5278 43.3333 3.1250 4.2023 7.6111 3.0976 0.7473
#> 4 1 G2 6.3250 43.3333 1.7500 3.0897 3.1000 2.6515 0.4824
#> 5 2 G2 5.8333 43.3333 3.0500 3.7692 14.6500 3.2121 0.6804
#> 6 3 G2 7.9074 43.3333 3.2778 3.6752 12.0000 3.0640 0.6471genMat<- gen.varcov(data = seldata[,3:9], genotypes = seldata[,2],
replication = seldata[,1])
print(genMat)
#> sypp dtf rpp ppr ppp spp pw
#> sypp 1.2566 0.3294 0.1588 0.2430 0.7350 0.1276 0.0926
#> dtf 0.3294 1.5602 0.1734 -0.3129 -0.2331 0.1168 0.0330
#> rpp 0.1588 0.1734 0.1325 -0.0316 0.3201 -0.0086 -0.0124
#> ppr 0.2430 -0.3129 -0.0316 0.2432 0.3019 -0.0209 0.0074
#> ppp 0.7350 -0.2331 0.3201 0.3019 0.9608 -0.0692 -0.0582
#> spp 0.1276 0.1168 -0.0086 -0.0209 -0.0692 0.0174 0.0085
#> pw 0.0926 0.0330 -0.0124 0.0074 -0.0582 0.0085 0.0103phenMat<- phen.varcov(data = seldata[,3:9], genotypes = seldata[,2],
replication = seldata[,1])
print(phenMat)
#> sypp dtf rpp ppr ppp spp pw
#> sypp 2.1465 0.1546 0.2320 0.2761 1.0801 0.1460 0.0875
#> dtf 0.1546 3.8372 0.1314 -0.4282 -0.4703 0.0585 -0.0192
#> rpp 0.2320 0.1314 0.2275 -0.0405 0.4635 0.0096 -0.0006
#> ppr 0.2761 -0.4282 -0.0405 0.4678 0.3931 -0.0205 0.0064
#> ppp 1.0801 -0.4703 0.4635 0.3931 4.2638 0.0632 -0.0245
#> spp 0.1460 0.0585 0.0096 -0.0205 0.0632 0.0836 0.0259
#> pw 0.0875 -0.0192 -0.0006 0.0064 -0.0245 0.0259 0.0226weightMat <- weight.mat(weight)
weightMat
#> ew h2
#> [1,] 1 0.6947
#> [2,] 1 0.3244
#> [3,] 1 0.6861
#> [4,] 1 0.7097
#> [5,] 1 0.8336
#> [6,] 1 0.2201
#> [7,] 1 0.5226- Genetic gain of Yield
GAY<- gen.advance(phen_mat = phenMat[1,1], gen_mat = genMat[1,1],
weight_mat = weightMat[1,1])
print(GAY)
#> [,1]
#> [1,] 1.76942For the construction of selection index we requires phenotypic &
genotypic variance-covariance matrix as well weight matrix.
comb.indices(ncomb = 1, pmat = phenMat, gmat = genMat, wmat = weight[,2:3], wcol = 1, GAY = GAY)
#> ID b GA PRE Rank
#> 1 1 0.5854 1.7694 100.0000 1
#> 2 2 0.4066 1.6431 92.8627 2
#> 3 3 0.5824 0.5731 32.3887 5
#> 4 4 0.5199 0.7336 41.4574 4
#> 5 5 0.2253 0.9599 54.2504 3
#> 6 6 0.2081 0.1241 7.0164 7
#> 7 7 0.4558 0.1413 7.9882 6rcomb.indices(ncomb = 1, i = 1, pmat = phenMat, gmat = genMat, wmat = weight[,2:3], wcol = 1, GAY = GAY)
#> ID b GA PRE Rank
#> 1 2 0.4066 1.6431 92.8627 1
#> 2 3 0.5824 0.5731 32.3887 4
#> 3 4 0.5199 0.7336 41.4574 3
#> 4 5 0.2253 0.9599 54.2504 2
#> 5 6 0.2081 0.1241 7.0164 6
#> 6 7 0.4558 0.1413 7.9882 5
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