Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

This function computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

Usage

kfolds2Chisqind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression glm model

Value

list

Individual PChisq vs number of components for the first group partition

list()

...

list

Individual PChisq vs number of components for the last group partition

Note

Use cv.plsRglm to create k-fold cross validated partial least squares regression glm models.

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisq, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Author

Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

Examples

# \donttest{
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=16,verbose=FALSE)
bbb2 <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=5,verbose=FALSE)
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#>       [,1]    [,2]     [,3]
#> 1 24.52456 11.3923 6.837075
#> 
#> [[1]][[2]]
#>       [,1]        [,2]        [,3]
#> 2 4.124502 0.007566232 0.005169207
#> 
#> [[1]][[3]]
#>       [,1]     [,2]     [,3]
#> 3 1.551301 1.917305 1.907019
#> 
#> [[1]][[4]]
#>       [,1]     [,2]        [,3]
#> 4 12.26717 1.278425 0.003572679
#> 
#> [[1]][[5]]
#>       [,1]     [,2]     [,3]
#> 5 4.315406 5.393862 4.249662
#> 
#> [[1]][[6]]
#>       [,1]     [,2]     [,3]
#> 6 6.209332 3.528493 1.696534
#> 
#> [[1]][[7]]
#>         [,1]     [,2]      [,3]
#> 7 0.08812214 0.256731 0.1887149
#> 
#> [[1]][[8]]
#>        [,1]     [,2]     [,3]
#> 8 0.9442012 0.240037 0.359011
#> 
#> [[1]][[9]]
#>        [,1]       [,2]       [,3]
#> 9 0.2203272 0.02253865 0.03724274
#> 
#> [[1]][[10]]
#>         [,1]       [,2]       [,3]
#> 10 0.6034613 0.05144812 0.01893424
#> 
#> [[1]][[11]]
#>         [,1]      [,2]      [,3]
#> 11 0.8275623 0.4409475 0.7467284
#> 
#> [[1]][[12]]
#>          [,1]         [,2]     [,3]
#> 12 0.03180524 1.455262e-05 4.794104
#> 
#> 
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 10.29691099  0.28157800  0.03439271
#> 
#> [[1]][[2]]
#> [1] 36.642707 11.837844  7.310443
#> 
#> [[1]][[3]]
#> [1] 2.5664845 0.2299003 0.2902721
#> 
#> [[1]][[4]]
#> [1] 5.447630 3.918006 7.854290
#> 
#> [[1]][[5]]
#> [1] 7.721843 3.310531 1.761714
#> 
#> 
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(object=ypine,dataX=Xpine,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbb2 <- cv.plsRglm(object=ypine,dataX=Xpine,nt=10,modele="pls-glm-gaussian",K=10,verbose=FALSE)
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.414566 1.806869 1.640698 1.552951
#> 
#> [[1]][[2]]
#> [1] 3.414549 2.626410 1.934197 2.065694
#> 
#> [[1]][[3]]
#> [1] 1.835293 1.349067 1.394961 1.303652
#> 
#> [[1]][[4]]
#> [1] 2.194565 1.701850 1.583206 1.347305
#> 
#> [[1]][[5]]
#> [1] 4.144954 3.965905 3.752847 3.991118
#> 
#> 
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#>  [1] 5.249999 4.756383 4.287571 4.547136 4.382030 4.606721 4.494257 4.484439
#>  [9] 4.540672 4.540350
#> 
#> [[1]][[2]]
#>  [1] 1.1137317 1.7219638 0.6268366 0.6718227 0.4594347 0.4086168 0.5840988
#>  [8] 0.6653888 0.6452377 0.6469058
#> 
#> [[1]][[3]]
#>  [1] 0.8703165 1.0047873 0.7994945 0.9143444 0.9313820 0.8944236 0.8440095
#>  [8] 0.8289457 0.7771644 0.7666672
#> 
#> [[1]][[4]]
#>  [1] 1.0719118 0.7457475 0.5650716 0.6633700 0.5679119 0.7838006 0.7884538
#>  [8] 0.8147564 0.8197531 0.8212560
#> 
#> [[1]][[5]]
#>  [1] 0.5140469 0.9940658 0.5681383 0.3667465 0.3961346 0.3228092 0.3095562
#>  [8] 0.2965725 0.3010821 0.3041078
#> 
#> [[1]][[6]]
#>  [1] 2.557322 2.279927 2.116355 2.145006 1.980049 1.979931 1.826305 1.804372
#>  [9] 1.800964 1.800720
#> 
#> [[1]][[7]]
#>  [1] 1.7654779 0.9806414 0.5152618 0.7254911 1.0845235 1.0297913 1.0177164
#>  [8] 0.9866440 0.9877018 0.9885207
#> 
#> [[1]][[8]]
#>  [1] 0.3866568 0.4647960 0.4659497 0.4659213 0.6611811 1.1216200 1.2080244
#>  [8] 1.2020985 1.1597944 1.1386756
#> 
#> [[1]][[9]]
#>  [1] 0.28074852 0.19201999 0.22241380 0.13739041 0.13258252 0.09078937
#>  [7] 0.09193145 0.08974185 0.10831094 0.10821061
#> 
#> [[1]][[10]]
#>  [1] 0.9008857 0.9194738 0.6583447 0.6273219 0.5694064 0.5483093 0.5507346
#>  [8] 0.6056700 0.5992925 0.5992653
#> 
#> 
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Pressind(bbbNA)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.2169996 1.6869248 0.9080029 0.9287743 1.0658864 1.0672493 0.8468892
#> [8] 0.8089848 0.7311545
#> 
#> [[1]][[2]]
#> [1] 3.719177 3.434162 3.012075 2.757994 2.872204 2.747240 2.800510 2.869313
#> [9] 3.868102
#> 
#> [[1]][[3]]
#> [1] 1.832169 1.550921 1.204722 1.580365 1.300763 1.223632 1.451868 1.477664
#> [9] 1.479606
#> 
#> [[1]][[4]]
#> [1] 0.7845042 0.4693467 0.3338035 0.3633763 0.2628447 0.2980458 0.3209635
#> [8] 0.2919794 0.3211792
#> 
#> [[1]][[5]]
#> [1]  1.4171891  1.8739234  2.1054461  0.8232368 32.5737503  2.8679986  3.6279457
#> [8] 20.8863833 17.1502058
#> 
#> [[1]][[6]]
#> [1] 0.5954218 0.9120790 1.2205720 1.1043135 1.1397225 1.1987425 1.1327736
#> [8] 1.1499350 1.1338614
#> 
#> [[1]][[7]]
#> [1] 2.333276 1.471815 1.778913 1.466270 1.274682 1.101338 1.087361 1.213602
#> [9] 1.167533
#> 
#> [[1]][[8]]
#> [1] 0.5993310 0.4289935 0.8210640 0.5906114 0.4902903 0.4237112 0.4385860
#> [8] 0.4050477 0.3964742
#> 
#> [[1]][[9]]
#> [1] 0.3262618 0.3705181 0.6105660 0.6618923 0.5375915 0.5397548 0.5709107
#> [8] 0.5802689 0.6147151
#> 
#> [[1]][[10]]
#> [1] 0.8603408 1.0351521 1.0962748 0.6685812 0.2043497 0.2035989 0.1327491
#> [8] 0.1882998 0.1179535
#> 
#> 
kfolds2Chisqind(bbbNA)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.2169996 1.6869248 0.9080029 0.9287743 1.0658864 1.0672493 0.8468892
#> [8] 0.8089848 0.7311545
#> 
#> [[1]][[2]]
#> [1] 3.719177 3.434162 3.012075 2.757994 2.872204 2.747240 2.800510 2.869313
#> [9] 3.868102
#> 
#> [[1]][[3]]
#> [1] 1.832169 1.550921 1.204722 1.580365 1.300763 1.223632 1.451868 1.477664
#> [9] 1.479606
#> 
#> [[1]][[4]]
#> [1] 0.7845042 0.4693467 0.3338035 0.3633763 0.2628447 0.2980458 0.3209635
#> [8] 0.2919794 0.3211792
#> 
#> [[1]][[5]]
#> [1]  1.4171891  1.8739234  2.1054461  0.8232368 32.5737503  2.8679986  3.6279457
#> [8] 20.8863833 17.1502058
#> 
#> [[1]][[6]]
#> [1] 0.5954218 0.9120790 1.2205720 1.1043135 1.1397225 1.1987425 1.1327736
#> [8] 1.1499350 1.1338614
#> 
#> [[1]][[7]]
#> [1] 2.333276 1.471815 1.778913 1.466270 1.274682 1.101338 1.087361 1.213602
#> [9] 1.167533
#> 
#> [[1]][[8]]
#> [1] 0.5993310 0.4289935 0.8210640 0.5906114 0.4902903 0.4237112 0.4385860
#> [8] 0.4050477 0.3964742
#> 
#> [[1]][[9]]
#> [1] 0.3262618 0.3705181 0.6105660 0.6618923 0.5375915 0.5397548 0.5709107
#> [8] 0.5802689 0.6147151
#> 
#> [[1]][[10]]
#> [1] 0.8603408 1.0351521 1.0962748 0.6685812 0.2043497 0.2035989 0.1327491
#> [8] 0.1882998 0.1179535
#> 
#> 
bbbNA2 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbbNA3 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian",
K=10,verbose=FALSE)
kfolds2Chisqind(bbbNA2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.622725 1.133662 1.131156 1.262468
#> 
#> [[1]][[2]]
#> [1] 1.817568 1.721046 1.328263 1.187040
#> 
#> [[1]][[3]]
#> [1] 2.633844 4.509174 2.975775 3.816601
#> 
#> [[1]][[4]]
#> [1] 0.9470552 2.1436244 1.4031261 1.1992904
#> 
#> [[1]][[5]]
#> [1] 6.238906 6.358348 5.702292 4.945047
#> 
#> 
kfolds2Chisqind(bbbNA3)
#> [[1]]
#> [[1]][[1]]
#> [1] 4.419860 4.250334 4.041978 3.930791 4.118144 4.611112 4.513158 4.494463
#> [9] 5.395129
#> 
#> [[1]][[2]]
#> [1] 0.1977676 0.9417647 0.7365591 0.5994182 0.7581636 0.7924363 0.6233532
#> [8] 0.6236648 0.0958404
#> 
#> [[1]][[3]]
#> [1] 1.4900206 1.3750245 0.7912330 0.7913849 1.0996923 1.4805663 1.5439061
#> [8] 1.2105181 1.1350007
#> 
#> [[1]][[4]]
#> [1] 1.5457864 0.8811869 0.3489742 0.3980700 0.3690931 0.5419888 0.6143899
#> [8] 0.6352883 0.7750405
#> 
#> [[1]][[5]]
#> [1] 0.2951755 0.4932952 0.5557877 0.4613454 0.5445926 0.5790580 0.5480461
#> [8] 0.4929761 0.5654871
#> 
#> [[1]][[6]]
#> [1] 0.6831417 1.3610809 0.9387628 1.1555575 0.8744560 0.5322855 0.4996885
#> [8] 0.6262281 1.3324691
#> 
#> [[1]][[7]]
#> [1]  1.6304186  1.2278593  0.3548124  2.9449266  2.5844455 11.0077115 11.1022632
#> [8] 10.6256660 11.5756748
#> 
#> [[1]][[8]]
#> [1] 0.8422700 0.4254764 0.3690328 0.3150925 0.2544780 0.6393860 0.6499880
#> [8] 0.8807291 0.8044254
#> 
#> [[1]][[9]]
#> [1] 1.348315 1.294047 1.024210 1.167113 1.422418 1.369221 1.374605 1.444214
#> [9] 1.478549
#> 
#> [[1]][[10]]
#> [1] 0.7550577 1.0385591 1.1049280 1.7828495 1.9598348 1.8927776 1.9122002
#> [8] 1.9486653 1.9620620
#> 
#> 
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2","bbbNA3"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family=binomial(),verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1]   87.94775  156.55848  468.29043 3797.38704
#> 
#> [[1]][[2]]
#> [1]    44.92822   310.85508  4351.65841 22512.82571
#> 
#> [[1]][[3]]
#> [1]   32.45370   44.39932  134.57205 1225.81836
#> 
#> [[1]][[4]]
#> [1]  24.13292  40.65945  65.81142 125.66658
#> 
#> [[1]][[5]]
#> [1]   29.19939  116.38839 2700.00963 6298.30769
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1]  23.71945  32.80686  48.37935 111.31990
#> 
#> [[1]][[2]]
#> [1]  33.04099  34.98864  54.70000 286.88415
#> 
#> [[1]][[3]]
#> [1]     29.04029     59.75615   6595.41494 215778.11902
#> 
#> [[1]][[4]]
#> [1]    68.42631   624.73363 14939.84428 47721.45914
#> 
#> [[1]][[5]]
#> [1]  25.20503  24.81453  54.27281 177.76315
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#>  [1]  23.81105  75.48124 259.38316 282.73303 189.52979 344.14547 533.00085
#>  [8] 794.52277 804.69347 863.23426
#> 
#> [[1]][[2]]
#>  [1] 13.22692 14.21606 14.18657 12.35091 13.30416 13.44207 14.03944 13.87701
#>  [9] 14.31051 14.55744
#> 
#> [[1]][[3]]
#>  [1] 3.616742e+01 1.126087e+02 1.115299e+03 6.063955e+03 1.368298e+04
#>  [6] 2.951455e+04 4.090504e+04 1.839531e+05 3.828123e+05 1.068556e+06
#> 
#> [[1]][[4]]
#>  [1]  6.237249  9.448145 11.737772 19.764538 29.927048 31.627313 27.614787
#>  [8] 26.067944 23.959075 23.673068
#> 
#> [[1]][[5]]
#>  [1]  8.288730  6.717653  5.525802  9.136546 11.712572 14.740887 12.818845
#>  [8] 12.461892 12.630076 13.584535
#> 
#> [[1]][[6]]
#>  [1]  20.03238  13.02489  20.26067  39.92648  71.76487  94.18123 158.23292
#>  [8] 181.19863 194.77805 187.07185
#> 
#> [[1]][[7]]
#>  [1]  10.96208  16.81468  34.04086  48.86097 111.38747 188.00031 268.61377
#>  [8] 309.94858 444.19019 532.26975
#> 
#> [[1]][[8]]
#>  [1]  31.89138  50.04075 106.28564 154.38394 124.75939 124.92804 121.89795
#>  [8] 119.80265 111.61665 108.35828
#> 
#> [[1]][[9]]
#>  [1]  10.64469  11.72032  16.85083  62.28414  82.69732 136.80218 183.94426
#>  [8] 202.94972 201.84011 192.47805
#> 
#> [[1]][[10]]
#>  [1]  30.25733  74.91938 202.09474 366.13928 609.60047 649.85026 693.93334
#>  [8] 759.35265 771.52925 702.18211
#> 
#> 
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,
modele="pls-glm-logistic",K=10,verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#>  [1]   20.41939   74.21808  613.62923 1293.17183 1313.47179 4799.65189
#>  [7] 5465.09410 6213.44447 8153.88005 7623.56702
#> 
#> [[1]][[2]]
#>  [1]  7.377795  9.461525 23.114180 31.328624 33.158089 33.629562 36.831677
#>  [8] 34.368925 37.034703 39.541355
#> 
#> [[1]][[3]]
#>  [1]  29.89616  25.12269  28.80606  49.91984  54.42617  43.20837  71.08081
#>  [8] 112.88387 147.18503 160.52100
#> 
#> [[1]][[4]]
#>  [1]    54.28878   115.19081  1816.82561  5210.83808  4692.38804  5082.27714
#>  [7]  6673.09262 14910.52317 38503.11928 64251.28102
#> 
#> [[1]][[5]]
#>  [1]    25.54499    50.82358   316.07965  1283.91411  3313.64454  5498.60454
#>  [7] 16416.08604 47467.71857 68872.13509 76757.87313
#> 
#> [[1]][[6]]
#>  [1]  20.17796  33.16328  70.95343 112.78555 119.17926 127.44522 114.05372
#>  [8] 117.21054 110.35475 105.22301
#> 
#> [[1]][[7]]
#>  [1]  8.509214  9.537985 12.976724 16.452572 19.854406 21.661372 22.028616
#>  [8] 19.876406 19.750512 19.293287
#> 
#> [[1]][[8]]
#>  [1] 11.32060 16.57948 18.81458 20.64505 22.99320 23.30307 21.18043 19.94628
#>  [9] 20.20394 19.93246
#> 
#> [[1]][[9]]
#>  [1] 10.061851 12.316944 12.123238  9.954245 11.571716 13.393430 14.512313
#>  [8] 14.437717 14.280243 14.355198
#> 
#> [[1]][[10]]
#>  [1] 12.09673 11.57221 13.36435 21.16308 33.53559 63.76180 74.09645 74.07616
#>  [9] 73.69777 66.27120
#> 
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }