plsRglm (version 1.5.1)

kfolds2Chisqind: Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

Description

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

Usage

kfolds2Chisqind(pls_kfolds)

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

Arguments

pls_kfolds

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

Author

Frédéric Bertrand

https://fbertran.github.io/homepage/

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. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47

See Also

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

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)
kfolds2Chisqind(bbb2)
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)
kfolds2Chisqind(bbb2)
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Pressind(bbbNA)
kfolds2Chisqind(bbbNA)
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)
kfolds2Chisqind(bbbNA3)
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))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,
modele="pls-glm-logistic",K=10,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))
# }