repeat_cv: Repeated cross-validation for sNPLS models

In sNPLS: NPLS Regression with L1 Penalization

Description

Performs repeated cross-validatiodn and represents results in a plot

Usage

repeat_cv(
  X_npls,
  Y_npls,
  ncomp = 1:3,
  samples = 20,
  keepJ = NULL,
  keepK = NULL,
  nfold = 10,
  times = 30,
  parallel = TRUE,
  method = "sNPLS",
  ...
)

Arguments

X_npls	A three-way array containing the predictors.
Y_npls	A matrix containing the response.
ncomp	A vector with the different number of components to test
samples	Number of samples for performing random search in continuous thresholding
keepJ	A vector with the different number of selected variables to test in discrete thresholding
keepK	A vector with the different number of selected 'times' to test in discrete thresholding
nfold	Number of folds for the cross-validation
times	Number of repetitions of the cross-validation
parallel	Should the computations be performed in parallel? Set up strategy first with future::plan()
method	Select between sNPLS, sNPLS-SR or sNPLS-VIP
...	Further arguments passed to cv_snpls

Value

A density plot with the results of the cross-validation and an (invisible) data.frame with these results

sNPLS documentation built on Jan. 13, 2021, 3:46 p.m.