enpls (version 5.6)

enpls.fs: Ensemble Partial Least Squares for Measuring Feature Importance

Description

Measuring feature importance with ensemble partial least squares.

Usage

enpls.fs(x, y, maxcomp = NULL, cvfolds = 5L, reptimes = 500L, method = c("mc", "boot"), ratio = 0.8, parallel = 1L)

Arguments

x

Predictor matrix.

y

Response vector.

maxcomp

Maximum number of components included within each model. If not specified, will use the maximum number possible (considering cross-validation and special cases where n is smaller than p).

cvfolds

Number of cross-validation folds used in each model for automatic parameter selection, default is 5.

reptimes

Number of models to build with Monte-Carlo resampling or bootstrapping.

method

Resampling method. "mc" (Monte-Carlo resampling) or "boot" (bootstrapping). Default is "mc".

ratio

Sampling ratio used when method = "mc".

parallel

Integer. Number of CPU cores to use. Default is 1 (not parallelized).

Value

A list containing two components:

• variable.importance - a vector of variable importance
• coefficient.matrix - original coefficient matrix

See Also

See enpls.od for outlier detection with ensemble partial least squares regressions. See enpls.fit for fitting ensemble partial least squares regression models.

Examples

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

set.seed(42)
fs = enpls.fs(x, y, reptimes = 50)
print(fs)
plot(fs)