plsRglm (version 1.3.0)

kfolds2CVinfos_lm: Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares models

Description

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

Usage

kfolds2CVinfos_lm(pls_kfolds, MClassed = FALSE, verbose = TRUE)

Arguments

pls_kfolds

an object computed using PLS_lm_kfoldcv

MClassed

should number of miss classed be computed

verbose

should infos be displayed ?

Value

list

table of fit statistics for first group partition

list()

…

list

table of fit statistics for last group partition

Details

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

References

Nicolas Meyer, Myriam Maumy-Bertrand et Fr<U+00E9>d<U+00E9>ric 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

# NOT RUN {
data(Cornell)
summary(cv.plsR(Y~.,data=Cornell,nt=10,K=6,verbose=FALSE))


data(pine)
summary(cv.plsR(x11~.,data=pine,nt=10,NK=3,verbose=FALSE),verbose=FALSE)
data(pineNAX21)
summary(cv.plsR(x11~.,data=pineNAX21,nt=10,NK=3,
verbose=FALSE),verbose=FALSE)


data(aze_compl)
summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,NK=3,
verbose=FALSE),MClassed=TRUE,verbose=FALSE)
# }
# NOT RUN {
# }