selection.index

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
#> sypp 1.25660210  0.32936305  0.158785900  0.242981986  0.73499020  0.127571993
#> dtf  0.32936305  1.56017847  0.173388420 -0.312908175 -0.23310004  0.116790239
#> rpp  0.15878590  0.17338842  0.132484364 -0.031596521  0.32014873 -0.008643769
#> ppr  0.24298199 -0.31290818 -0.031596521  0.243231727  0.30192365 -0.020860985
#> ppp  0.73499020 -0.23310004  0.320148725  0.301923650  0.96076644 -0.069172364
#> spp  0.12757199  0.11679024 -0.008643769 -0.020860985 -0.06917236  0.017410958
#> pw   0.09261588  0.03298807 -0.012353519  0.007352443 -0.05824420  0.008560105
#>                pw
#> sypp  0.092615879
#> dtf   0.032988075
#> rpp  -0.012353519
#> ppr   0.007352443
#> ppp  -0.058244197
#> spp   0.008560105
#> pw    0.010304709

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
#> sypp 2.14648906  0.15455221  0.2319728887  0.276121850  1.08008088  0.145985907
#> dtf  0.15455221  3.83717336  0.1313373906 -0.428164534 -0.47026101  0.058467819
#> rpp  0.23197289  0.13133739  0.2274791728 -0.040450725  0.46352988  0.009592048
#> ppr  0.27612185 -0.42816453 -0.0404507247  0.467797686  0.39314225 -0.020464804
#> ppp  1.08008088 -0.47026101  0.4635298765  0.393142248  4.26374874  0.063241030
#> spp  0.14598591  0.05846782  0.0095920481 -0.020464804  0.06324103  0.083572855
#> pw   0.08747568 -0.01919207 -0.0006013091  0.006372357 -0.02452747  0.025910429
#>                 pw
#> sypp  0.0874756848
#> dtf  -0.0191920675
#> rpp  -0.0006013091
#> ppr   0.0063723573
#> ppp  -0.0245274713
#> spp   0.0259104291
#> pw    0.0226032987

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,] 1.7694

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.1     GA      PRE Rank
#> 1  1 0.5854 1.7694 100.0015    1
#> 2  2 0.4066 1.6431  92.8628    2
#> 3  3 0.5824 0.5731  32.3867    5
#> 4  4 0.5200 0.7337  41.4634    4
#> 5  5 0.2253 0.9599  54.2494    3
#> 6  6 0.2083 0.1242   7.0220    7
#> 7  7 0.4559 0.1414   7.9914    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.1     GA     PRE Rank
#> 1  2 0.4066 1.6431 92.8628    1
#> 2  3 0.5824 0.5731 32.3867    4
#> 3  4 0.5200 0.7337 41.4634    3
#> 4  5 0.2253 0.9599 54.2494    2
#> 5  6 0.2083 0.1242  7.0220    6
#> 6  7 0.4559 0.1414  7.9914    5