Predict selection index scores
Examples
gmat <- gen_varcov(seldata[, 3:9], seldata[, 2], seldata[, 1])
pmat <- phen_varcov(seldata[, 3:9], seldata[, 2], seldata[, 1])
cindex <- lpsi(ncomb = 1, pmat = pmat, gmat = gmat, wmat = weight[, -1], wcol = 1)
predict_selection_score(cindex, data = seldata[, 3:9], genotypes = seldata[, 2])
#> Genotypes I_1 I_1_Rank I_2 I_2_Rank I_3 I_3_Rank I_4
#> 1 G1 3.456263 19 10.24955 24.0 4.539159 1 1.491622
#> 2 G2 4.224499 13 10.38266 16.0 4.003627 4 1.582573
#> 3 G3 2.875212 23 10.78199 6.0 2.885280 22 1.115753
#> 4 G4 4.425748 9 10.58233 9.0 3.791595 8 1.494010
#> 5 G5 4.429979 8 10.51578 11.0 3.794073 7 1.540463
#> 6 G6 3.876003 17 10.31611 20.5 3.072927 18 1.278441
#> 7 G7 3.095577 22 10.31611 20.5 3.242285 16 1.188075
#> 8 G8 3.491337 18 10.98166 4.0 3.630514 12 1.404757
#> 9 G9 5.418307 1 10.31611 17.0 3.366143 15 1.794447
#> 10 G10 2.431071 25 10.51578 11.0 2.891178 21 1.370714
#> 11 G11 4.468465 7 10.71544 7.0 3.214727 17 1.536046
#> 12 G12 4.172392 14 10.31611 20.5 2.584034 23 1.681126
#> 13 G13 4.280248 11 10.38267 15.0 2.962747 19 1.577450
#> 14 G14 5.155877 2 10.31611 20.5 3.878331 5 1.916752
#> 15 G15 2.710617 24 10.64889 8.0 1.941247 25 1.790706
#> 16 G16 4.163949 15 10.44923 13.5 4.114500 3 1.534393
#> 17 G17 4.406126 10 10.44923 13.5 3.717250 10 1.429515
#> 18 G18 3.948932 16 10.31611 20.5 3.705999 11 1.804663
#> 19 G19 4.921996 4 11.18133 2.0 3.747533 9 1.158209
#> 20 G20 4.829214 5 10.11645 25.0 2.450609 24 1.905515
#> 21 G21 4.937996 3 10.98167 3.0 3.472159 13 1.438559
#> 22 G22 4.823550 6 11.78033 1.0 4.212883 2 1.327702
#> 23 G23 3.328132 21 10.51578 11.0 3.447427 14 1.264469
#> 24 G24 3.453568 20 10.31611 20.5 2.940741 20 1.072726
#> 25 G25 4.245742 12 10.84856 5.0 3.827380 6 1.043280
#> I_4_Rank I_5 I_5_Rank I_6 I_6_Rank I_7 I_7_Rank
#> 1 13 2.425143 10 4.018543 24 1.279820 15
#> 2 7 3.137633 1 4.314709 19 1.133963 24
#> 3 23 1.681191 24 4.623779 9 1.322424 12
#> 4 12 2.886623 3 4.692408 6 1.226627 17
#> 5 9 2.873967 4 4.472748 11 1.224183 18
#> 6 19 2.100105 14 4.792693 3 1.571220 8
#> 7 21 1.880386 19 4.293589 21 1.270798 16
#> 8 16 2.432325 9 4.180110 23 1.072249 25
#> 9 4 2.350293 11 4.409919 15 1.779793 1
#> 10 17 1.842155 22 3.817587 25 1.168735 22
#> 11 10 2.040485 16 4.603191 10 1.741888 3
#> 12 6 1.879490 20 4.430459 14 1.657243 5
#> 13 8 2.000945 18 4.376523 17 1.578488 7
#> 14 1 2.664352 7 4.465015 12 1.390967 10
#> 15 5 1.734347 23 4.249222 22 1.136030 23
#> 16 11 2.996593 2 4.378360 16 1.284456 14
#> 17 15 2.841061 5 4.345350 18 1.219923 19
#> 18 3 2.700421 6 4.303110 20 1.209523 20
#> 19 22 2.101719 13 5.091759 1 1.705988 4
#> 20 2 2.220664 12 5.015204 2 1.771962 2
#> 21 14 2.567523 8 4.663748 8 1.388523 11
#> 22 18 2.010891 17 4.735373 5 1.565080 9
#> 23 20 1.867931 21 4.667470 7 1.175126 21
#> 24 24 1.539349 25 4.454528 13 1.302187 13
#> 25 25 2.051917 15 4.767899 4 1.584377 6