nbcomp.bootplsRglm {bootPLS}  R Documentation 
Provides a wrapper for the bootstrap function boot
from the
boot
R package.
Implements nonparametric bootstraps for PLS
Generalized Linear Regression models by (Y,T) resampling to select the
number of components.
nbcomp.bootplsRglm( object, typeboot = "boot_comp", R = 250, statistic = coefs.plsRglm.CSim, sim = "ordinary", stype = "i", stabvalue = 1e+06, ... )
object 
An object of class 
typeboot 
The type of bootstrap. ( 
R 
The number of bootstrap replicates. Usually this will be a single
positive integer. For importance resampling, some resamples may use one set
of weights and others use a different set of weights. In this case 
statistic 
A function which when applied to data returns a vector
containing the statistic(s) of interest. 
sim 
A character string indicating the type of simulation required.
Possible values are 
stype 
A character string indicating what the second argument of

stabvalue 
A value to hard threshold bootstrap estimates computed from atypical resamplings. Especially useful for Generalized Linear Models. 
... 
Other named arguments for 
More details on bootstrap techniques are available in the help of the
boot
function.
An object of class "boot"
. See the Value part of the help of
the function boot
.
Jérémy Magnanensi, Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
A new bootstrapbased stopping criterion in PLS component construction,
J. Magnanensi, M. MaumyBertrand, N. Meyer and F. Bertrand (2016), in The Multiple Facets of Partial Least Squares and Related Methods,
doi: 10.1007/9783319406435_18
A new universal resamplestable bootstrapbased stopping criterion for PLS component construction,
J. Magnanensi, F. Bertrand, M. MaumyBertrand and N. Meyer, (2017), Statistics and Compututing, 27, 757–774.
doi: 10.1007/s1122201696514
New developments in Sparse PLS regression, J. Magnanensi, M. MaumyBertrand, N. Meyer and F. Bertrand, (2021), Frontiers in Applied Mathematics and Statistics, accepted.
set.seed(314) library(plsRglm) data(aze_compl, package="plsRglm") Xaze_compl<aze_compl[,2:34] yaze_compl<aze_compl$y dataset < cbind(y=yaze_compl,Xaze_compl) modplsglm < plsRglm::plsRglm(y~.,data=dataset,10,modele="plsglmfamily", family = binomial) comp_aze_compl.bootYT < nbcomp.bootplsRglm(modplsglm, R=250) boxplots.bootpls(comp_aze_compl.bootYT) confints.bootpls(comp_aze_compl.bootYT) plots.confints.bootpls(confints.bootpls(comp_aze_compl.bootYT),typeIC = "BCa") comp_aze_compl.permYT < nbcomp.bootplsRglm(modplsglm, R=250, sim="permutation") boxplots.bootpls(comp_aze_compl.permYT) confints.bootpls(comp_aze_compl.permYT, typeBCa=FALSE) plots.confints.bootpls(confints.bootpls(comp_aze_compl.permYT, typeBCa=FALSE))