repeat_cv: Repeated cross-validation for sNPLS models
In David-Hervas/sNPLS: NPLS Regression with L1 Penalization

repeat_cv

R Documentation

Repeated cross-validation for sNPLS models

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,
  threshold_j = c(0, 1),
  threshold_k = c(0, 1),
  nfold = 10,
  times = 30,
  parallel = TRUE,
  method = "sNPLS",
  metric = "RMSE",
  ...
)

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
`threshold_j`	Vector with threshold min and max values on Wj. Scaled between [0, 1)
`threshold_k`	Vector with threshold min and max values on Wk. Scaled between [0, 1)
`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
`metric`	Select between RMSE or AUC (for 0/1 response)
`...`	Further arguments passed to cv_snpls