nbcomp.bootplsR {bootPLS} | R Documentation |
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.
nbcomp.bootplsR( Y, X, R = 500, sim = "ordinary", ncpus = 1, parallel = "no", typeBCa = TRUE, verbose = TRUE )
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 |
sim |
A character string indicating the type of simulation required.
Possible values are |
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? |
More details on bootstrap techniques are available in the help of the
boot
function.
A numeric, the number of components selected by the bootstrap.
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.
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)