penalised_pred: Internal lassoenet functions

In MokyoZhou/lassoenet: An Interactive Implementation of Penalised Regressions

Description

Internal lassoenet functions

Usage

penalised_pred(data = data, parallel = parallel, response = response,
  x.indices = x.indices, err.curves = err.curves,
  type.lambda = type.lambda)

Arguments

data	A well-cleaned data.frame.
parallel	parallelisation.
response	The location of the response within the data.frame.
x.indices	The locations of the predictors withint the data.frame.
err.curves	The number of error curves to fit.
type.lambda	Either "lambda.min" or "lambda.1se".

Value

A vector of outputs and some plots related to the best model. The return from this function will enter the interactiver function.

Details

These are not intended for use by users. This function is the overall wrapper for the prediction focused path. It takes returns from both the prediction_Lasso and prediction_ElasticNet and put these through the comparison functions prediction.nonsplit.result and prediction.split.result. The return from this function will enter the interactiver function.

Author(s)

Mokyo Zhou

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