plsRglm (version 0.3.3)

kfolds2Chisqind: Computes individual Predicted Chisquare for kfold cross validated partial least squares regression models.

Description

This function computes individual Predicted Chisquare for kfold cross validated partial least squares regression models.

Usage

kfolds2Chisqind(pls_kfolds)

Arguments

pls_kfolds

a kfold cross validated partial least squares regression glm model

Value

• list
  Individual PChisq vs number of components for the first group partition
• ...
  ...
• list
  Individual PChisq vs number of components for the last group partition

References

Nicolas Meyer, Myriam Maumy-Bertrand et Fr�d�ric{Fr'ed'eric} Bertrand (2010). Comparaison de la r�gression{r'egression} PLS et de la r�gression{r'egression} logistique PLS : application aux donn�es{donn'ees} d'all�lotypage{d'all'elotypage}. Journal de la Soci�t� Fran�aise de Statistique, 151(2), pages 1-18. http://smf4.emath.fr/Publications/JSFdS/151_2/pdf/sfds_jsfds_151_2_1-18.pdf

See Also

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

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- PLS_glm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=16)
bbb2 <- PLS_glm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=5)
kfolds2Chisqind(bbb)
kfolds2Chisqind(bbb2)
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- PLS_glm_kfoldcv(dataY=ypine,dataX=Xpine,nt=4,modele="pls-glm-gaussian")
bbb2 <- PLS_glm_kfoldcv(dataY=ypine,dataX=Xpine,nt=10,modele="pls-glm-gaussian",K=10)
kfolds2Chisqind(bbb)
kfolds2Chisqind(bbb2)
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- PLS_glm_kfoldcv(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10)
kfolds2Pressind(bbbNA)
kfolds2Chisqind(bbbNA)
bbbNA2 <- PLS_glm_kfoldcv(dataY=ypine,dataX=XpineNAX21,nt=4,modele="pls-glm-gaussian")
bbbNA3 <- PLS_glm_kfoldcv(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian",K=10)
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
bbb <- PLS_glm_kfoldcv(dataY=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic")
bbb2 <- PLS_glm_kfoldcv(dataY=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-logistic",K=10)
kfolds2Chisqind(bbb)
kfolds2Chisqind(bbb2)
rm(list=c("Xaze_compl","yaze_compl","bbb","bbb2"))