cv.enpls: Cross Validation for Ensemble Partial Least Squares...

In wind22zhu/enpls1.2: Ensemble Partial Least Squares Regression

Description

This function performs 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 folds - default is 5.
verbose	shall we print the cross validation process
...	other arguments that can be passed to enpls.en

Details

This function performs k-fold cross validation for ensemble partial least squares regression.

Value

A list containing four components:

• ypred - a matrix containing two columns: real y and predicted y

• residual - cross validation result (y.pred - y.real)

• RMSE - RMSE

• R2 - R2

Author(s)

Min-feng Zhu <[e-mail]>, Nan Xiao <[e-mail]>

References

Dongsheng Cao, Yizeng Liang, Qingsong Xu, Yifeng Yun, and Hongdong Li. "Toward better QSAR/QSPR modeling: simultaneous outlier detection and variable selection using distribution of model features." Journal of computer-aided molecular design 25, no. 1 (2011): 67–80.

See Also

See enpls.en for ensemble PLS regression.

Examples

data(alkanes)
x = alkanes$x
y = alkanes$y

set.seed(42)
cv.enpls.fit = cv.enpls(x, y, MCtimes = 10)
print(cv.enpls.fit)
plot(cv.enpls.fit)

wind22zhu/enpls1.2 documentation built on May 4, 2019, 6:31 a.m.