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Cross Validation for Ensemble Partial Least Squares Regression

Source: R/cv.enpls.R
cv.enpls.Rd

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

Usage

cv.enpls(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 PLS model, not the individual PLS models. To control the CV folds for single PLS models, please use the argument cvfolds.

verbose

Shall we print out the progress of cross-validation?

...

Arguments to be passed to enpls.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.

See also

See enpls.fit for ensemble partial least squares regressions.

Author

Nan Xiao <https://nanx.me>

Examples

data("alkanes")
x <- alkanes$x
y <- alkanes$y

set.seed(42)
cvfit <- cv.enpls(x, y, reptimes = 10)
#> Beginning fold 1 
#> Beginning fold 2 
#> Beginning fold 3 
#> Beginning fold 4 
#> Beginning fold 5 
print(cvfit)
#> Cross Validation Result for Ensemble Partial Least Squares
#> ---
#> RMSE = 3.6716
#> MAE = 2.511028
#> Rsquare = 0.999960
plot(cvfit)