Extracts and computes information criteria and fits statistics for kfold cross validated partial least squares beta regression models

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

Usage

kfolds2CVinfos_beta(pls_kfolds, MClassed = FALSE)

Arguments

pls_kfolds

an object computed using PLS_beta_kfoldcv

MClassed

should number of miss classed be computed

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

Frédéric Bertrand, Nicolas Meyer, Michèle Beau-Faller, Karim El Bayed, Izzie-Jacques Namer, Myriam Maumy-Bertrand (2013). Régression Bêta PLS. Journal de la Société Française de Statistique, 154(3):143-159. https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215

See also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from kfold cross validation.

Author

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

Examples

if (FALSE) { # \dontrun{
data("GasolineYield",package="betareg")
bbb <- PLS_beta_kfoldcv_formula(yield~.,data=GasolineYield,nt=3,modele="pls-beta")
kfolds2CVinfos_beta(bbb)
} # }