Description Usage Arguments Value Examples
Performs cross-validation for a sNPLS model
1 2 3 4 5 6 7 8 9 10 11 12 |
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 for discrete thresholding |
keepK |
A vector with the different number of selected 'times' to test for discrete thresholding |
nfold |
Number of folds for the cross-validation |
parallel |
Should the computations be performed in parallel? Set up strategy first with |
method |
Select between sNPLS, sNPLS-SR or sNPLS-VIP |
... |
Further arguments passed to sNPLS |
A list with the best parameters for the model and the CV error
1 2 3 4 5 6 7 8 9 10 11 | ## Not run:
X_npls<-array(rpois(7500, 10), dim=c(50, 50, 3))
Y_npls<-matrix(2+0.4*X_npls[,5,1]+0.7*X_npls[,10,1]-0.9*X_npls[,15,1]+
0.6*X_npls[,20,1]- 0.5*X_npls[,25,1]+rnorm(50), ncol=1)
#Grid search for discrete thresholding
cv1<- cv_snpls(X_npls, Y_npls, ncomp=1:2, keepJ = 1:3, keepK = 1:2, parallel = FALSE)
#Random search for continuous thresholding
cv2<- cv_snpls(X_npls, Y_npls, ncomp=1:2, samples=20, parallel = FALSE)
## End(Not run)
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