nbcomp.bootsgpls.para {bootPLS} | R Documentation |
Number of components for SGPLS using (Y,T) bootstrap (parallel version)
nbcomp.bootsgpls.para( x, y, fold = 10, eta, R, scale.x = TRUE, maxnt = 10, br = TRUE, ftype = "iden", ncpus = 1, plot.it = TRUE, typeBCa = TRUE, stabvalue = 1e+06, verbose = TRUE )
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. |
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". |
ncpus |
Number of cpus for parallel computing. |
plot.it |
Plot the results. |
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. |
List of four: error matrix, eta optimal, K optimal and the matrix of results.
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(4619) data(prostate, package="spls") nbcomp.bootsgpls.para((prostate$x)[,1:30], prostate$y, R=250, eta=0.2, maxnt=1, typeBCa = FALSE) set.seed(4619) data(prostate, package="spls") nbcomp.bootsgpls.para(prostate$x, prostate$y, R=250, eta=c(0.2,0.6), typeBCa = FALSE)