enplscv: Ensemble Partial Least Squares Cross-Valdidation for feature...

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

Description

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

Usage

enplscv(trainx, trainy, cv.fold = 5, scale = "log", step = 0.5,
  recursive = FALSE, MCtimes = 10, ...)

Arguments

trainx	matrix or data frame containing columns of predictor
trainy	vector of response, must have length equal to the number of rows in trainx.
cv.fold	number of folds in the cross-validation
scale	if "log", reduce a fixed proportion (step) of variables at each step, otherwise reduce step variables at a time.
step	if log=TRUE, the fraction of variables to remove at each step, else remove this many variables at a time
recursive	whether variable importance is (re-)assessed at each step of variable reduction
MCtimes	times of Monte-Carlo
...	other arguments passed on to enpls.en

Details

This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.

Value

A list with the following components:

• n.var - vector of number of variables used at each step.

• error.cv - corresponding vector of error rates or MSEs at each step.

• predicted - list of n.var components, each containing the predicted values from the cross-validation.

• res - list of n.var components, each containing the sum of coefficient values from the cross-validation.

• imp - list of n.var components, each containing the sum of coefficient values from the cross-validation.

Author(s)

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

References

Svetnik, V., Liaw, A., Tong, C. and Wang, T., "Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules", MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.

See Also

See enpls.en for feature selection with ensemble PLS. See enpls.en for ensemble PLS regression

Examples

data(logS)
x = logS$x
y = logS$y

result = enplscv(x, y, recursive = TRUE)
with(result, plot(n.var, error.cv, type="b", lwd=2))

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