# Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models. This function computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models. ```r kfolds2Chisqind(pls_kfolds) ``` ## Arguments - `pls_kfolds`: a k-fold cross validated partial least squares regression glm model ## Returns - **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. ## Examples ```r 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")) ``` ## 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](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. ## Author(s) Frédéric Bertrand frederic.bertrand@utt.fr [https://fbertran.github.io/homepage/](https://fbertran.github.io/homepage/)

kfolds2Chisqind function