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

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

Usage

kfolds2Chisq(pls_kfolds)

Arguments

pls_kfolds

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

Value

list

Total Predicted Chisquare vs number of components for the first group partition

list()

...

list

Total Predicted Chisquare 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.

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. https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/47/

See also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisqind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Author

Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

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)
kfolds2Chisq(bbb)
#> [[1]]
#> [1] NA NA NA
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] NA NA NA
#> 
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)
kfolds2Chisq(bbb)
#> [[1]]
#> [1] NA NA NA NA
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#>  [1] NA NA NA NA NA NA NA NA NA NA
#> 
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Press(bbbNA)
#> [[1]]
#> [1] 14.57505 14.63146 13.91856 13.41291 15.14871 15.27842 47.43333 59.65936
#> [9] 15.49328
#> 
kfolds2Chisq(bbbNA)
#> [[1]]
#> [1] 14.57505 14.63146 13.91856 13.41291 15.14871 15.27842 47.43333 59.65936
#> [9] 15.49328
#> 
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)
kfolds2Chisq(bbbNA2)
#> [[1]]
#> [1] NA NA NA NA
#> 
kfolds2Chisq(bbbNA3)
#> [[1]]
#> [1] NA NA NA NA NA NA NA NA NA
#> 
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
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family="binomial",verbose=FALSE))
#> [[1]]
#> [1] NA NA NA NA
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [1] NA NA NA NA
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> [[1]]
#>  [1] NA NA NA NA NA NA NA NA NA NA
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-logistic",
K=10,verbose=FALSE))
#> [[1]]
#>  [1] NA NA NA NA NA NA NA NA NA NA
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }