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

Description Usage Arguments Value Author(s) See Also Examples

Measuring feature importance with ensemble sparse partial least squares.

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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)

`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).

A list containing two components:

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

Nan Xiao <https://nanx.me>

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.

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)