nbcomp.bootspls.para {bootPLS}

Title

Description

Title

Usage

nbcomp.bootspls.para(
  x,
  y,
  fold = 10,
  eta,
  R = 500,
  maxnt = 10,
  kappa = 0.5,
  select = "pls2",
  fit = "simpls",
  scale.x = TRUE,
  scale.y = FALSE,
  plot.it = TRUE,
  typeBCa = TRUE,
  ncpus = 1,
  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.

maxnt

Maximum number of components allowed in a spls model.

kappa

Parameter to control the effect of the concavity of the objective function and the closeness of original and surrogate direction vectors. kappa is relevant only when responses are multivariate. kappa should be between 0 and 0.5. Default is 0.5.

select

PLS algorithm for variable selection. Alternatives are "pls2" or "simpls". Default is "pls2".

fit

PLS algorithm for model fitting. Alternatives are "kernelpls", "widekernelpls", "simpls", or "oscorespls". Default is "simpls".

scale.x

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

scale.y

Scale responses by dividing each response variable by its sample standard deviation?

plot.it

Plot the results.

typeBCa

Include computation for BCa type interval.

ncpus

Number of cpus for parallel computing.

verbose

Displays information on the algorithm.

Value

list of 3: mspemat matrix of results, eta.opt numeric value, K.opt numeric value)

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(314)
data(pine, package = "plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
nbcomp.bootspls.para(x=Xpine,y=ypine,eta=.2, maxnt=1)

set.seed(314)
data(pine, package = "plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
nbcomp.bootspls.para(x=Xpine,y=ypine,eta=c(.2,.6))