enpls.fit: Ensemble Partial Least Squares Regression

In enpls: Ensemble Partial Least Squares Regression

Description

Ensemble partial least squares regression.

Usage

enpls.fit(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 all partial least squares model objects.

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fs for measuring feature importance with ensemble partial least squares regressions. See enpls.od for outlier detection with ensemble partial least squares regressions.

Examples

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

set.seed(42)
fit <- enpls.fit(x, y, reptimes = 50)
print(fit)
predict(fit, newx = x)

enpls documentation built on May 18, 2019, 9:02 a.m.