enpls (version 5.6)

enspls.fit: Ensemble Sparse Partial Least Squares Regression

Description

Ensemble sparse partial least squares regression.

Usage

enspls.fit(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 all sparse partial least squares model objects.

See Also

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

Examples

data("logd1k")
x = logd1k$x
y = logd1k$y

set.seed(42)
fit = enspls.fit(x, y, reptimes = 5, maxcomp = 3,
                 alpha = c(0.3, 0.6, 0.9))
print(fit)
predict(fit, newx = x)