Number of missclassified individuals for k-fold cross validated partial least squares regression models.

This function indicates the total number of missclassified individuals for k-fold cross validated partial least squares regression models.

Usage

kfolds2Mclassed(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model used on binary data

Value

list

Total number of missclassified individuals vs number of components for the first group partition

list()

...

list

Total number of missclassified individuals vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression 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 and kfolds2Mclassedind 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(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Mclassed(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=1,verbose=FALSE))
#> [[1]]
#>  [1] 44 48 41 39 39 39 39 38 38 38
#> 
kfolds2Mclassed(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=2,verbose=FALSE))
#> [[1]]
#>  [1] 49 46 41 45 44 45 44 46 46 46
#> 
#> [[2]]
#>  [1] 49 48 48 50 48 49 50 52 51 51
#> 
rm(list=c("Xaze_compl","yaze_compl"))
# }