Performs repeated cross-validatiodn and represents results in a plot
repeat_cv(
X_npls,
Y_npls,
ncomp = 1:3,
samples = 20,
keepJ = NULL,
keepK = NULL,
nfold = 10,
times = 30,
parallel = TRUE,
method = "sNPLS",
...
)A three-way array containing the predictors.
A matrix containing the response.
A vector with the different number of components to test
Number of samples for performing random search in continuous thresholding
A vector with the different number of selected variables to test in discrete thresholding
A vector with the different number of selected 'times' to test in discrete thresholding
Number of folds for the cross-validation
Number of repetitions of the cross-validation
Should the computations be performed in parallel? Set up strategy first with future::plan()
Select between sNPLS, sNPLS-SR or sNPLS-VIP
Further arguments passed to cv_snpls
A density plot with the results of the cross-validation and an (invisible) data.frame with these results