enpls (version 6.1)

enpls.fit: 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.

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

# NOT RUN {
data("alkanes")
x <- alkanes$x
y <- alkanes$y

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