coefs.plsR.adapt.ncomp {bootPLS} | R Documentation |
Bootstrap (Y,X) for the coefficients with number of components updated for each resampling.
coefs.plsR.adapt.ncomp( dataset, i, R = 1000, ncpus = 1, parallel = "no", verbose = FALSE )
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. |
Numeric vector: first value is the number of components, the remaining values are the coefficients the variables computed for that number of components.
Jérémy Magnanensi, Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
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.
set.seed(314) ncol=5 xran=matrix(rnorm(30*ncol),30,ncol) coefs.plsR.adapt.ncomp(xran,sample(1:30)) coefs.plsR.adapt.ncomp(xran,sample(1:30),ncpus=2,parallel="multicore")