nbcomp.bootsgpls {bootPLS}

Number of components for SGPLS using (Y,T) bootstrap

Description

Number of components for SGPLS using (Y,T) bootstrap

Usage

nbcomp.bootsgpls(
  x,
  y,
  fold = 10,
  eta,
  R,
  scale.x = TRUE,
  maxnt = 10,
  plot.it = TRUE,
  br = TRUE,
  ftype = "iden",
  typeBCa = TRUE,
  stabvalue = 1e+06,
  verbose = TRUE
)

Arguments

x

Matrix of predictors.

y

Vector or matrix of responses.

fold

Number of fold for cross-validation

eta

Thresholding parameter. eta should be between 0 and 1.

R

Number of resamplings.

scale.x

Scale predictors by dividing each predictor variable by its sample standard deviation?

maxnt

Maximum number of components allowed in a spls model.

plot.it

Plot the results.

br

Apply Firth's bias reduction procedure?

ftype

Type of Firth's bias reduction procedure. Alternatives are "iden" (the approximated version) or "hat" (the original version). Default is "iden".

typeBCa

Include computation for BCa type interval.

stabvalue

A value to hard threshold bootstrap estimates computed from atypical resamplings.

verbose

Additionnal information on the algorithm.

Value

List of four: error matrix, eta optimal, K optimal and the matrix of results.

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

set.seed(4619)
data(prostate, package="spls")
nbcomp.bootsgpls((prostate$x)[,1:30], prostate$y, R=250, eta=0.2, maxnt=1, typeBCa = FALSE)

set.seed(4619)
data(prostate, package="spls")
nbcomp.bootsgpls(prostate$x, prostate$y, R=250, eta=c(0.2,0.6), typeBCa = FALSE)