enpls (version 5.6)

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, ...)

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

See Also

See enspls.fit for ensemble sparse partial least squares regressions.

Examples

# This example takes one minute to run
## Not run: 
# data("logd1k")
# x = logd1k$x
# y = logd1k$y
# 
# set.seed(42)
# cvfit = cv.enspls(x, y, reptimes = 10)
# print(cvfit)
# plot(cvfit)## End(Not run)