bootPLS (version 1.0.1)

permcoefs.plsRglm.CSim: Permutation bootstrap (Y,T) function for PLSGLR

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRglm.CSim(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = NULL,
  maxcoefvalues,
  ifbootfail
)

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Numeric vector of the components computed using a permutation resampling.

Arguments

dataRepYtt

Dataset with tt components to resample

ind

indices for resampling

nt

number of components to use

modele

type of modele to use, see plsRglm. Not used, please specify the family instead.

family

glm family to use, see plsRglm

maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples

ifbootfail

value to return if the estimation fails on a bootstrap sample

Author

Jérémy Magnanensi, Frédéric Bertrand

https://fbertran.github.io/homepage/

References

A new bootstrap-based stopping criterion in PLS component construction, J. Magnanensi, M. Maumy-Bertrand, N. Meyer and F. Bertrand (2016), in The Multiple Facets of Partial Least Squares and Related Methods, tools:::Rd_expr_doi("10.1007/978-3-319-40643-5_18")

A new universal resample-stable bootstrap-based stopping criterion for PLS component construction, J. Magnanensi, F. Bertrand, M. Maumy-Bertrand and N. Meyer, (2017), Statistics and Computing, 27, 757–774. tools:::Rd_expr_doi("10.1007/s11222-016-9651-4")

New developments in Sparse PLS regression, J. Magnanensi, M. Maumy-Bertrand, N. Meyer and F. Bertrand, (2021), Frontiers in Applied Mathematics and Statistics, tools:::Rd_expr_doi("10.3389/fams.2021.693126")
.

Examples

set.seed(314)
library(plsRglm)
data(aze_compl, package="plsRglm")
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
dataset <- cbind(y=yaze_compl,Xaze_compl)
modplsglm <- plsRglm::plsRglm(y~.,data=dataset,4,modele="pls-glm-logistic")
dataRepYtt <- cbind(y = modplsglm$RepY, modplsglm$tt)
permcoefs.plsRglm.CSim(dataRepYtt, sample(1:nrow(dataRepYtt)), 4, 
family = binomial, maxcoefvalues=10, ifbootfail=0)