nbcomp.bootplsR {bootPLS}

Non-parametric (Y,T) Bootstrap for selecting the number of components in PLSR models

Description

Provides a wrapper for the bootstrap function boot from the boot R package.
Implements non-parametric bootstraps for PLS Regression models by (Y,T) resampling to select the number of components.

Usage

nbcomp.bootplsR(
  Y,
  X,
  R = 500,
  sim = "ordinary",
  ncpus = 1,
  parallel = "no",
  typeBCa = TRUE,
  verbose = TRUE
)

Arguments

Y

Vector of response.

X

Matrix of predictors.

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 R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.

sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".

ncpus

integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.

parallel

The type of parallel operation to be used (if any). If missing, the default is taken from the option "boot.parallel" (and if that is not set, "no").

typeBCa

Compute BCa type intervals ?

verbose

Display info during the run of algorithm?

Details

More details on bootstrap techniques are available in the help of the boot function.

Value

A numeric, the number of components selected by the bootstrap.

Author(s)

Jérémy Magnanensi, Frédéric Bertrand
[e-mail]
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, 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 Compututing, 27, 757–774. 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, accepted.

Examples

data(pine, package="plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
res <- nbcomp.bootplsR(ypine, Xpine)
nbcomp.bootplsR(ypine, Xpine, typeBCa=FALSE)

nbcomp.bootplsR(ypine, Xpine, typeBCa=FALSE, verbose=FALSE)
try(nbcomp.bootplsR(ypine, Xpine, sim="permutation"))
nbcomp.bootplsR(ypine, Xpine, sim="permutation", typeBCa=FALSE)