Non-parametric (Y,T) Bootstrap for selecting the number of components in PLSR models

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.

Usage

nbcomp.bootplsR(
  Y,
  X,
  R = 500,
  sim = "ordinary",
  ncpus = 1,
  parallel = "no",
  typeBCa = TRUE,
  verbose = TRUE
)

Arguments

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 R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
sim: A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".
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?

Value

A numeric, the number of components selected by the bootstrap.

Details

More details on bootstrap techniques are available in the help of the boot function.

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

data(pine, package="plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
res <- nbcomp.bootplsR(ypine, Xpine)
#> [1] 1
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] 2
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] 3
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] "Optimal number of components: K = 2"
nbcomp.bootplsR(ypine, Xpine, typeBCa=FALSE)
#> [1] 1
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] 2
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] 3
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] "Optimal number of components: K = 2"
#> [1] 2
# \donttest{
nbcomp.bootplsR(ypine, Xpine, typeBCa=FALSE, verbose=FALSE)
#> [1] 2
try(nbcomp.bootplsR(ypine, Xpine, sim="permutation"))
#> [1] 1
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> Error in bca.ci(boot.out, conf, index[1L], L = L, t = t.o, t0 = t0.o,  : 
#>   estimated adjustment 'w' is infinite
nbcomp.bootplsR(ypine, Xpine, sim="permutation", typeBCa=FALSE)
#> [1] 1
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> [1] "Optimal number of components: K = 0"
#> [1] 0
# }