en.robust: Internal lassoenet function

In MokyoZhou/lassoenet: An Interactive Implementation of Penalised Regressions

Description

Internal lassoenet function

Usage

en.robust(x = x, y = y, alpha.seq = alpha.seq, err.curves = err.curves,
  result.matrix = result.matrix, parallel = parallel)

Arguments

x	A model.matrix for the predictors.
y	A vector of response values.
alpha.seq	The alpha sequence for the Elastic Net
err.curves	The number of error curves to be fitted. Default is 0.
result.matrix	A matrix for storing results.
parallel	Parallelisation

Value

A vector of results for the best Elastic Net model along with some plots of the error curves, under the condition where the full dataset has been used for modelling. The return from this function will enter prediction_ElasticNet.

Details

These are not intended for use by users. This function is one of the main engines for the Elastic Net computation. This function is used when the user does not want to split the dataset into a training and a test set. This together with the function no_split_en form the computation operator for the Elastic Net when using the full dataset. The return from this function will enter prediction_ElasticNet for futher wrapping.

Author(s)

Mokyo Zhou

MokyoZhou/lassoenet documentation built on May 20, 2019, 11:38 a.m.