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

In enpls: Ensemble Partial Least Squares Regression

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

Author(s)

Nan Xiao <https://nanx.me>

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)

enpls documentation built on Aug. 8, 2025, 6:47 p.m.