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

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

Usage

kfolds2Chisq(pls_kfolds)

Arguments

pls_kfolds

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

Value

list

Total Predicted Chisquare vs number of components for the first group partition

list()

...

list

Total Predicted Chisquare 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, kfolds2Chisqind, 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)
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 55.70774 24.52966 20.84377
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 62.06902 21.17915 19.65666
#> 
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)
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 14.18541 12.66755 11.29117 12.16633
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#>  [1] 16.01037 15.04104 12.82465 13.46816 13.22914 14.40349 14.61850 14.95845
#>  [9] 14.86388 14.91391
#> 
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Press(bbbNA)
#> [[1]]
#> [1] 13.67592 14.91544 14.81330 12.40331 12.06289 12.11392 12.83221 21.45890
#> [9] 26.94021
#> 
kfolds2Chisq(bbbNA)
#> [[1]]
#> [1] 13.67592 14.91544 14.81330 12.40331 12.06289 12.11392 12.83221 21.45890
#> [9] 26.94021
#> 
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)
kfolds2Chisq(bbbNA2)
#> [[1]]
#> [1] 13.20970 31.65945 28.43378 22.55204
#> 
kfolds2Chisq(bbbNA3)
#> [[1]]
#> [1] 14.32418 14.87616 11.89853 12.62791 14.28605 13.89974 21.94249 12.59579
#> [9] 19.89630
#> 
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
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family="binomial",verbose=FALSE))
#> [[1]]
#> [1]  186.4604  268.9234 1574.2969 6408.2692
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [1]     247.6534     580.9059   24264.5656 5442717.3419
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> [[1]]
#>  [1] 1.986115e+02 4.625618e+02 1.210752e+04 1.416175e+05 5.670987e+05
#>  [6] 9.457270e+05 3.882446e+06 1.077786e+07 7.300684e+07 1.367845e+08
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-logistic",
K=10,verbose=FALSE))
#> [[1]]
#>  [1]    193.2747    396.3554   4373.4478  23786.8329  46570.8844  63133.7421
#>  [7]  96978.2077 179158.3425 180320.0528 210506.6641
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }