plsRglm (version 0.3.3)

kfolds2Chisq: Computes Predicted Chisquare for kfold cross validated partial least squares regression models.

Description

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

Usage

kfolds2Chisq(pls_kfolds)

Arguments

pls_kfolds

a kfold cross validated partial least squares regression glm model

Value

• list
  Total Predicted Chisquare vs number of components for the first group partition
• ...
  ...
• list
  Total Predicted Chisquare 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, kfolds2Chisqind, 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)
kfolds2Chisq(bbb)
kfolds2Chisq(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)
kfolds2Chisq(bbb)
kfolds2Chisq(bbb2)
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- PLS_glm_kfoldcv(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10)
kfolds2Press(bbbNA)
kfolds2Chisq(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)
kfolds2Chisq(bbbNA2)
kfolds2Chisq(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)
kfolds2Chisq(bbb)
kfolds2Chisq(bbb2)
rm(list=c("Xaze_compl","yaze_compl","bbb","bbb2"))