enspls.fs: Ensemble Sparse Partial Least Squares for Measuring Feature...

In enpls: Ensemble Partial Least Squares Regression

Ensemble Sparse Partial Least Squares for Measuring Feature Importance

Description

Measuring feature importance with ensemble sparse partial least squares.

Usage

enspls.fs(
  x,
  y,
  maxcomp = 5L,
  cvfolds = 5L,
  alpha = seq(0.2, 0.8, 0.2),
  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 5 by default.
cvfolds	Number of cross-validation folds used in each model for automatic parameter selection, default is 5.
alpha	Parameter (grid) controlling sparsity of the model. If not specified, default is seq(0.2, 0.8, 0.2).
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 enspls.od for outlier detection with ensemble sparse partial least squares regressions. See enspls.fit for fitting ensemble sparse partial least squares regression models.

Examples

data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
fs <- enspls.fs(x, y, reptimes = 5, maxcomp = 2)
print(fs, nvar = 10)
plot(fs, nvar = 10)
plot(fs, type = "boxplot", limits = c(0.05, 0.95), nvar = 10)

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