cv.enspls: Cross Validation for Ensemble Sparse Partial Least Squares Regression
Description
K-fold cross validation for ensemble sparse partial least squares regression.
Usage
cv.enspls(x, y, nfolds = 5L, verbose = TRUE, ...)
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
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?
# This example takes one minute to runif (FALSE) {
data("logd1k")
x <- logd1k$x
y <- logd1k$y
set.seed(42)
cvfit <- cv.enspls(x, y, reptimes = 10)
print(cvfit)
plot(cvfit)
}