README

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

install.packages("selection.index")

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

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

selection.index