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.

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@utt.fr
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
# }