en.robust.split: Internal lassoenet function

Description Usage Arguments Value Details Author(s)

View source: R/en.robust.split.R

Description

Internal lassoenet function

Usage

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

Arguments

x

A model.matrix of the preditors for the training data.

y

A vector of response values on the training set.

test

A model.matrix of the predictors for the testing data.

yv

A vector of response values on the testing set.

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 divided into a traning and a test set. 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 would like to split the dataset into a training and a test set. This together with the function split_en forms the computation operator for the Elastic Net when using the a traning and a testing set. 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.