prediction.split.result: Internal lassoenet functions

In MokyoZhou/lassoenet: An Interactive Implementation of Penalised Regressions

Description

Internal lassoenet functions

Usage

prediction.split.result(best.lasso.result = best.lasso.result,
  best.EN.result = best.EN.result, data = data, x.indices = x.indices,
  response = response, alph = alph, splits = splits,
  parallel = parallel)

Arguments

best.lasso.result	A vector contains the outputs from the function prediction_Lasso.
best.EN.result	A vector contains the outputs from the function prediction_ElasticNet.
data	A well-cleaned data.frame.
x.indices	Locations of the predictors within the data.frame.
response	Location of the response within the data.frame.
alph	An internal argument.
splits	A two element vector with the first element being the training proportion and the second element being the testing proportion.
parallel	Parallelsation.

Value

A vector of outputs and some plots related to the best model.

Details

These are not intended for use by users. This function provides an automatic comparison between the Lasso and the Elastic Net model based on the user's preferred prediction metric. The return from this function will then enter the penalised_pred function.

Author(s)

Mokyo Zhou

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