Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.
Source:R/kfolds2Chisqind.R
kfolds2Chisqind.RdThis function computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.
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] 5.270827 1.511911 1.430687
#>
#> [[1]][[2]]
#> [1] 0.6394877 0.4095096 1.0584582
#>
#> [[1]][[3]]
#> [1] 26.502867 13.361154 8.250662
#>
#> [[1]][[4]]
#> [1] 1.985502 2.149674 2.040927
#>
#> [[1]][[5]]
#> [1] 11.849695 2.460984 6.748015
#>
#>
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] 3.403086 4.775572 4.519409 5.002448
#>
#> [[1]][[2]]
#> [1] 1.824134 2.162432 1.596622 1.359406
#>
#> [[1]][[3]]
#> [1] 2.587970 1.691074 1.391184 1.443778
#>
#> [[1]][[4]]
#> [1] 1.266668 1.572788 1.260169 1.344553
#>
#> [[1]][[5]]
#> [1] 3.749667 3.031469 1.618427 1.615485
#>
#>
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 0.8869803 0.8201235 0.7967343 0.7834618 0.7443253 0.7626616 0.6772168
#> [8] 0.6074659 0.6187567 0.6415269
#>
#> [[1]][[2]]
#> [1] 4.727405 4.636497 4.341603 4.546572 4.759693 5.077514 5.229127 5.471423
#> [9] 5.487885 5.488157
#>
#> [[1]][[3]]
#> [1] 1.077921 1.151543 1.204886 1.550415 1.729665 1.970757 1.940021 2.001377
#> [9] 2.000931 1.989826
#>
#> [[1]][[4]]
#> [1] 1.5252320 1.1816437 0.3847527 0.3050577 0.2951873 0.2509198 0.3295281
#> [8] 0.3587540 0.3784665 0.3785523
#>
#> [[1]][[5]]
#> [1] 0.650217 1.536378 1.535037 1.563574 1.454718 1.198908 1.244873 1.296059
#> [9] 1.271407 1.272678
#>
#> [[1]][[6]]
#> [1] 0.5208920 0.1635685 0.2768095 0.3350192 0.4934330 0.4361214 0.4658536
#> [8] 0.4806951 0.4824459 0.4827584
#>
#> [[1]][[7]]
#> [1] 0.006366112 1.201509729 0.769069823 0.770990708 0.805811006 0.908296996
#> [7] 0.883070758 0.858064121 0.854869289 0.854843256
#>
#> [[1]][[8]]
#> [1] 0.05687795 0.16551094 0.37338030 0.32373596 0.30780311 0.17014380
#> [7] 0.16251227 0.17074300 0.17904736 0.17889468
#>
#> [[1]][[9]]
#> [1] 2.514685 1.264220 1.034154 1.011181 1.009631 1.368947 1.651633 1.731609
#> [9] 1.770509 1.760526
#>
#> [[1]][[10]]
#> [1] 1.0707442 0.3930249 0.2207084 0.5900025 0.9509311 0.7454288 0.7667870
#> [8] 0.7697079 0.7585861 0.7579902
#>
#>
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] 3.641936 2.715888 2.815467 1.642285 1.704489 1.733388 1.742250 1.746528
#> [9] 1.703715
#>
#> [[1]][[2]]
#> [1] 0.2565236 0.5619357 0.6086918 0.5428673 0.5498746 0.6248340 0.8229439
#> [8] 0.8311556 0.9891515
#>
#> [[1]][[3]]
#> [1] 1.9831732 2.8387181 2.8462283 1.4337590 1.2091250 0.9997404 1.2264522
#> [8] 1.2905409 1.4198860
#>
#> [[1]][[4]]
#> [1] 2.791337 2.883241 2.413335 2.479880 2.666350 2.654463 2.730224 2.694283
#> [9] 2.493838
#>
#> [[1]][[5]]
#> [1] 1.392256 1.558197 1.293175 0.928126 1.770689 3.311278 4.164474
#> [8] 18.322802 19.378881
#>
#> [[1]][[6]]
#> [1] 0.6020566 0.2924178 0.2213609 0.2406571 0.4891932 0.4846383 0.5742382
#> [8] 0.5750435 0.7232366
#>
#> [[1]][[7]]
#> [1] 0.6683048 1.3658249 1.8962920 0.8775053 0.8153458 0.7861862 0.3650173
#> [8] 0.3374038 1.0031900
#>
#> [[1]][[8]]
#> [1] 0.9329137 0.6598429 0.3628448 0.4212905 0.4961495 0.7362372 0.8537199
#> [8] 0.8657835 0.8664770
#>
#> [[1]][[9]]
#> [1] 0.5409445 0.8358791 0.5884406 0.5631689 0.5332073 0.4849964 0.3831118
#> [8] 0.3429699 0.4277211
#>
#> [[1]][[10]]
#> [1] 0.6720491 0.9014411 1.2582868 1.4982960 1.3938938 1.4452417 1.4827895
#> [8] 1.4351993 1.5402511
#>
#>
kfolds2Chisqind(bbbNA)
#> [[1]]
#> [[1]][[1]]
#> [1] 3.641936 2.715888 2.815467 1.642285 1.704489 1.733388 1.742250 1.746528
#> [9] 1.703715
#>
#> [[1]][[2]]
#> [1] 0.2565236 0.5619357 0.6086918 0.5428673 0.5498746 0.6248340 0.8229439
#> [8] 0.8311556 0.9891515
#>
#> [[1]][[3]]
#> [1] 1.9831732 2.8387181 2.8462283 1.4337590 1.2091250 0.9997404 1.2264522
#> [8] 1.2905409 1.4198860
#>
#> [[1]][[4]]
#> [1] 2.791337 2.883241 2.413335 2.479880 2.666350 2.654463 2.730224 2.694283
#> [9] 2.493838
#>
#> [[1]][[5]]
#> [1] 1.392256 1.558197 1.293175 0.928126 1.770689 3.311278 4.164474
#> [8] 18.322802 19.378881
#>
#> [[1]][[6]]
#> [1] 0.6020566 0.2924178 0.2213609 0.2406571 0.4891932 0.4846383 0.5742382
#> [8] 0.5750435 0.7232366
#>
#> [[1]][[7]]
#> [1] 0.6683048 1.3658249 1.8962920 0.8775053 0.8153458 0.7861862 0.3650173
#> [8] 0.3374038 1.0031900
#>
#> [[1]][[8]]
#> [1] 0.9329137 0.6598429 0.3628448 0.4212905 0.4961495 0.7362372 0.8537199
#> [8] 0.8657835 0.8664770
#>
#> [[1]][[9]]
#> [1] 0.5409445 0.8358791 0.5884406 0.5631689 0.5332073 0.4849964 0.3831118
#> [8] 0.3429699 0.4277211
#>
#> [[1]][[10]]
#> [1] 0.6720491 0.9014411 1.2582868 1.4982960 1.3938938 1.4452417 1.4827895
#> [8] 1.4351993 1.5402511
#>
#>
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] 5.091976 5.668531 7.992906 8.948263
#>
#> [[1]][[2]]
#> [1] 2.312984 1.441495 1.488056 1.738745
#>
#> [[1]][[3]]
#> [1] 1.553196 2.551305 2.206062 2.006054
#>
#> [[1]][[4]]
#> [1] 3.749187 3.766897 3.202139 3.041355
#>
#> [[1]][[5]]
#> [1] 0.7240605 4.1309348 3.9388575 4.4043879
#>
#>
kfolds2Chisqind(bbbNA3)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.1794345 1.1577211 0.5312897 0.7023919 0.9409709 1.1335568 1.5944448
#> [8] 1.0753626 1.0515777
#>
#> [[1]][[2]]
#> [1] 1.0068477 0.5015760 0.2035914 0.6133291 0.9741998 1.2965126 1.4742455
#> [8] 1.6903695 0.8815662
#>
#> [[1]][[3]]
#> [1] 1.872407 3.523068 2.687198 4.599433 5.535495 6.715902 6.318444 3.070310
#> [9] 4.747822
#>
#> [[1]][[4]]
#> [1] 2.680572 1.515843 1.609055 1.487503 1.489059 1.568317 1.673913 1.541438
#> [9] 1.695382
#>
#> [[1]][[5]]
#> [1] 0.9831575 1.0232133 0.9547098 0.8725859 0.9718035 0.9406500 0.9357887
#> [8] 0.9024237 1.0029078
#>
#> [[1]][[6]]
#> [1] 0.7022996 1.1960171 0.9894453 0.9033905 0.9318882 1.0201916 1.1004181
#> [8] 1.1643403 1.2396577
#>
#> [[1]][[7]]
#> [1] 2.893779 2.984157 2.908782 2.823959 2.736246 2.848856 2.692257 2.665015
#> [9] 2.664210
#>
#> [[1]][[8]]
#> [1] 0.1939123 0.3389540 0.2573845 0.3813121 0.4585366 0.3817952 0.5314726
#> [8] 0.6209207 0.7590313
#>
#> [[1]][[9]]
#> [1] 0.3042696 0.1721206 0.3637156 0.3045094 0.3176336 0.2976240 0.2469196
#> [8] 0.2111337 0.1470695
#>
#> [[1]][[10]]
#> [1] 0.9630187 0.8365556 0.5605143 0.5959843 0.4754921 0.4567590 0.5664468
#> [8] 0.6122550 0.6728351
#>
#>
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] 26.90170 36.40484 38.51725 38.87166
#>
#> [[1]][[2]]
#> [1] 32.61727 62.73811 148.57031 2226.98288
#>
#> [[1]][[3]]
#> [1] 26.01683 95.75682 614.09120 759.75406
#>
#> [[1]][[4]]
#> [1] 52.94904 114.88423 989.88793 4112.44160
#>
#> [[1]][[5]]
#> [1] 29.93034 34.08810 53.25737 113.07159
#>
#>
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1] 47.59640 80.94215 113.33396 183.61306
#>
#> [[1]][[2]]
#> [1] 74.79705 207.64307 335.03829 752.79300
#>
#> [[1]][[3]]
#> [1] 47.09273 74.93908 266.56083 1377.30320
#>
#> [[1]][[4]]
#> [1] 31.73475 54.16956 93.65354 133.03732
#>
#> [[1]][[5]]
#> [1] 26.46269 47.19208 98.07683 167.21246
#>
#>
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> [[1]]
#> [[1]][[1]]
#> [1] 30.37905 131.61933 295.07586 449.43316 465.95404 622.34039 594.66732
#> [8] 552.40620 541.41116 461.34886
#>
#> [[1]][[2]]
#> [1] 12.54716 14.91610 24.01796 31.44286 28.58675 31.12905 30.44918 30.91007
#> [9] 30.29058 32.20295
#>
#> [[1]][[3]]
#> [1] 19.25542 30.38544 37.77202 128.59382 150.09659 243.47865 413.22428
#> [8] 642.23408 700.38212 741.01251
#>
#> [[1]][[4]]
#> [1] 4.490421e+01 1.732139e+02 1.051098e+04 6.309520e+05 9.187609e+06
#> [6] 1.949896e+09 4.503771e+15 9.010711e+15 1.801494e+16 2.251800e+16
#>
#> [[1]][[5]]
#> [1] 16.966171 7.672902 9.012394 11.454132 11.474949 14.098260 14.840711
#> [8] 14.254955 13.197875 12.721494
#>
#> [[1]][[6]]
#> [1] 7.444570 6.333452 8.840397 10.865892 15.210250 16.900480 21.882404
#> [8] 27.511089 27.772383 28.208200
#>
#> [[1]][[7]]
#> [1] 12.57589 15.83949 31.31110 123.82153 135.82582 122.53225 96.60512
#> [8] 75.62408 88.89131 99.92953
#>
#> [[1]][[8]]
#> [1] 36.78350 58.18266 124.57593 241.31382 404.07471 501.45468 515.44248
#> [8] 504.49675 437.32656 388.22272
#>
#> [[1]][[9]]
#> [1] 9.679721 13.718786 15.340246 27.822643 49.031872 78.163101
#> [7] 152.973442 264.490718 300.856504 253.825429
#>
#> [[1]][[10]]
#> [1] 23.52748 35.34624 53.95292 112.67853 182.33070 201.99742 217.28400
#> [8] 287.72665 298.27349 288.38705
#>
#>
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,
modele="pls-glm-logistic",K=10,verbose=FALSE))
#> [[1]]
#> [[1]][[1]]
#> [1] 17.16111 20.54780 42.96324 50.41764 67.83271 72.45856 87.38555 86.40900
#> [9] 93.93810 92.43602
#>
#> [[1]][[2]]
#> [1] 17.488320 10.097871 8.616709 15.236710 22.035719 26.949970 25.709833
#> [8] 27.980402 27.834312 27.664695
#>
#> [[1]][[3]]
#> [1] 9.314635 10.629045 24.802254 47.837962 66.721316 107.068433
#> [7] 124.279462 134.636647 138.991686 137.468731
#>
#> [[1]][[4]]
#> [1] 81.06815 210.21352 3665.07542 19566.78091 33527.51517 32555.33438
#> [7] 53366.06399 47213.13269 64700.19582 68795.91094
#>
#> [[1]][[5]]
#> [1] 23.37379 50.96192 367.69449 344.31163 401.72190 504.27506 762.19446
#> [8] 810.07412 668.38914 617.19564
#>
#> [[1]][[6]]
#> [1] 22.49615 33.89744 60.19610 149.92402 187.79812 217.12727 224.88363
#> [8] 214.55649 211.12614 227.56614
#>
#> [[1]][[7]]
#> [1] 16.26003 19.50735 21.72989 20.71220 20.25186 17.73315 20.04467 27.39599
#> [9] 26.88810 26.90085
#>
#> [[1]][[8]]
#> [1] 12.09134 13.00128 21.37000 29.14232 30.79322 54.11291 79.85591
#> [8] 159.90995 162.14637 169.92531
#>
#> [[1]][[9]]
#> [1] 25.68953 40.57266 39.28939 91.36514 87.23446 80.54601 80.31430 88.24899
#> [9] 90.94341 89.85898
#>
#> [[1]][[10]]
#> [1] 16.42260 36.44474 73.14231 290.03249 575.97750 1119.96831
#> [7] 1669.19671 1947.92863 1990.44154 1704.00586
#>
#>
rm(list=c("Xaze_compl","yaze_compl"))
# }