Bootstrap (Y,X) for the coefficients with number of components updated for each resampling.

Usage

coefs.plsR.adapt.ncomp(
  dataset,
  i,
  R = 1000,
  ncpus = 1,
  parallel = "no",
  verbose = FALSE
)

Arguments

dataset: Dataset to use.
i: Vector of resampling.
R: Number of resamplings to find the number of components.
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").
verbose: Suppress information messages.

Value

Numeric vector: first value is the number of components, the remaining values are the coefficients the variables computed for that number of components.

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 Computing, 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, doi:10.3389/fams.2021.693126
.

Author

Jérémy Magnanensi, Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

Examples

set.seed(314)
ncol=5
xran=matrix(rnorm(30*ncol),30,ncol)
coefs.plsR.adapt.ncomp(xran,sample(1:30))
#> Loading required namespace: plsdof
#> [1]  0.0000000 -0.3144043  0.0000000  0.0000000  0.0000000  0.0000000
# \donttest{
coefs.plsR.adapt.ncomp(xran,sample(1:30),ncpus=2,parallel="multicore")
#> [1]  0.0000000 -0.3144043  0.0000000  0.0000000  0.0000000  0.0000000
# }