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

Source: R/kfolds2Chisq.R
kfolds2Chisq.Rd

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://www.numdam.org/item/JSFS_2010__151_2_1_0/

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] 55.70774 24.52966 20.84377
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 69.55157 27.20145 26.54985
#> 
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] 12.89898 12.44225 10.44155 10.35581
#> 
kfolds2Chisq(bbb2)
#> [[1]]
#>  [1] 13.55527 12.30310 10.45758 10.94869 11.48481 12.23844 12.10153 11.98165
#>  [9] 12.11042 12.10683
#> 
                  
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]  14.05196  14.10976  13.88910  11.23984  11.38487  13.36423  20.93071
#> [8]  39.65877 178.10012
#> 
kfolds2Chisq(bbbNA)
#> [[1]]
#> [1]  14.05196  14.10976  13.88910  11.23984  11.38487  13.36423  20.93071
#> [8]  39.65877 178.10012
#> 
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] 13.88537 13.76352 10.31822 12.90021
#> 
kfolds2Chisq(bbbNA3)
#> [[1]]
#> [1] 14.193035 13.426074 12.152154 12.708522 12.930586 10.740865  9.980292
#> [8] 10.665961 10.920715
#> 
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]   212.6266   367.4410  1786.6113 12084.5509
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
#> [[1]]
#> [1]    213.0669   2087.8089  75802.6131 604360.4772
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
#> [[1]]
#>  [1]     205.0276     399.5351    5357.1214   19652.8925   22438.1409
#>  [6]   45101.3065   91601.8014  359566.1556  899715.6936 1600524.6150
#> 
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-logistic",
K=10,verbose=FALSE))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> [[1]]
#>  [1] 2.225894e+02 5.840101e+02 1.268689e+04 3.661034e+05 1.466490e+08
#>  [6] 3.505241e+12 4.505082e+15 1.351087e+16 2.251800e+16 2.251800e+16
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }