kfolds2CVinfos_glm: Extracts and computes information criteria and fits...

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models

Description

This function extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models for both formula or classic specifications of the model.

Usage

kfolds2CVinfos_glm(pls_kfolds, MClassed = FALSE, verbose = TRUE)

Arguments

pls_kfolds	an object computed using cv.plsRglm
MClassed	should number of miss classed be computed ?
verbose	should infos be displayed ?

Details

The Mclassed option should only set to TRUE if the response is binary.

Value

list	table of fit statistics for first group partition
list()	...
list	table of fit statistics for last group partition

Note

Use summary and cv.plsRglm instead.

Author(s)

Frédéric Bertrand
[e-mail]
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, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(Cornell)
summary(cv.plsRglm(Y~.,data=Cornell,
nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls",verbose=FALSE),MClassed=TRUE)


data(aze_compl)
summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=gaussian(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-logistic",
verbose=FALSE),MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=binomial(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)


if(require(chemometrics)){
data(hyptis)
hyptis
yhyptis <- factor(hyptis$Group,ordered=TRUE)
Xhyptis <- as.data.frame(hyptis[,c(1:6)])
options(contrasts = c("contr.treatment", "contr.poly"))
modpls2 <- plsRglm(yhyptis,Xhyptis,6,modele="pls-glm-polr")
modpls2$Coeffsmodel_vals
modpls2$InfCrit
modpls2$Coeffs
modpls2$std.coeffs

table(yhyptis,predict(modpls2$FinalModel,type="class"))

modpls3 <- PLS_glm(yhyptis[-c(1,2,3)],Xhyptis[-c(1,2,3),],3,modele="pls-glm-polr",
dataPredictY=Xhyptis[c(1,2,3),],verbose=FALSE)

summary(cv.plsRglm(factor(Group,ordered=TRUE)~.,data=hyptis[,-c(7,8)],nt=4,K=10,
random=TRUE,modele="pls-glm-polr",keepcoeffs=TRUE,verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
}

plsRglm documentation built on March 31, 2023, 11:10 p.m.