Cross Validation for Ensemble Sparse Partial Least Squares Regression

K-fold cross validation for ensemble sparse partial least squares regression.

Usage

cv.enspls(x, y, nfolds = 5L, verbose = TRUE, ...)

Arguments

x: Predictor matrix.
y: Response vector.
nfolds: Number of cross-validation folds, default is 5. Note that this is the CV folds for the ensemble sparse PLS model, not the individual sparse PLS models. To control the CV folds for single sparse PLS models, please use the argument cvfolds.
verbose: Shall we print out the progress of cross-validation?
...: Arguments to be passed to enspls.fit.

Value

A list containing:

ypred - a matrix containing two columns: real y and predicted y
residual - cross validation result (y.pred - y.real)
RMSE - RMSE
MAE - MAE
Rsquare - Rsquare

Note

To maximize the probablity that each observation can be selected in the test set (thus the prediction uncertainty can be measured), please try setting a large reptimes.

Author

Nan Xiao <https://nanx.me>

Examples

# This example takes one minute to run
if (FALSE) {
data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
cvfit <- cv.enspls(x, y, reptimes = 10)
print(cvfit)
plot(cvfit)
}