Title

Title

nbcomp.bootspls.para(
  x,
  y,
  fold = 10,
  eta,
  R = 500,
  maxnt = 10,
  kappa = 0.5,
  select = "pls2",
  fit = "simpls",
  scale.x = TRUE,
  scale.y = FALSE,
  plot.it = TRUE,
  typeBCa = TRUE,
  ncpus = 1,
  verbose = TRUE
)

Arguments

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.
maxnt	Maximum number of components allowed in a spls model.
kappa	Parameter to control the effect of the concavity of the objective function and the closeness of original and surrogate direction vectors. kappa is relevant only when responses are multivariate. kappa should be between 0 and 0.5. Default is 0.5.
select	PLS algorithm for variable selection. Alternatives are "pls2" or "simpls". Default is "pls2".
fit	PLS algorithm for model fitting. Alternatives are "kernelpls", "widekernelpls", "simpls", or "oscorespls". Default is "simpls".
scale.x	Scale predictors by dividing each predictor variable by its sample standard deviation?
scale.y	Scale responses by dividing each response variable by its sample standard deviation?
plot.it	Plot the results.
typeBCa	Include computation for BCa type interval.
ncpus	Number of cpus for parallel computing.
verbose	Displays information on the algorithm.

Value

list of 3: mspemat matrix of results, eta.opt numeric value, K.opt numeric value)

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

set.seed(314)
data(pine, package = "plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
nbcomp.bootspls.para(x=Xpine,y=ypine,eta=.2, maxnt=1)
#> [1] "eta = 0.2"
#> [1] 2
#> 
#> Optimal parameters: eta = 0.2, K = 1
#> $mspemat
#>                         
#> eta= 0.2 ; K= 1 1.090242
#> 
#> $eta.opt
#> [1] 0.2
#> 
#> $K.opt
#> [1] 1
#> 
# \donttest{
set.seed(314)
data(pine, package = "plsRglm")
Xpine<-pine[,1:10]
ypine<-log(pine[,11])
nbcomp.bootspls.para(x=Xpine,y=ypine,eta=c(.2,.6))
#> [1] "eta = 0.2"
#> [1] 2
#> [1] 3
#> [1] "eta = 0.6"
#> [1] 2
#> [1] 3
#> 
#> Optimal parameters: eta = 0.2, K = 2
#> $mspemat
#>                         
#> eta= 0.2 ; K= 2 1.276262
#> eta= 0.6 ; K= 2 1.413876
#> 
#> $eta.opt
#> [1] 0.2
#> 
#> $K.opt
#> result.1 
#>        2 
#> 
# }