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.
Installation
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")
Example
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 0.6471
Genotypic Variance-Covariance Matrix
genMat<- 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 0.0103
Phenotypic Variance-Covariance Matrix
phenMat<- 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 0.0226
Weight Matrix - Data is included in package weight
weightMat <- 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,] 1.76942
Construction of selection index/indices
For the construction of selection index we requires phenotypic & genotypic variance-covariance matrix as well weight matrix.
Construction of all possible selection indices for a character combinations
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 6
Construction of selection indices by removing desired single character from the combinations
rcomb.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