split_lasso: Internal lassoenet function

Description Usage Arguments Value Details Author(s)

View source: R/split_lasso.R

Description

Internal lassoenet function

Usage

1
2
3
split_lasso(x.train = x.train, y.train = y.train, x.test = x.test,
  y.test = y.test, err.curves = 0, type.lambda = type.lambda,
  parallel = parallel)

Arguments

x.train

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

y.train

A vector of response values on the training set.

x.test

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

y.test

A vector of response values on the testing set.

err.curves

The number of error curves to be fitted. Default is 0.

type.lambda

Either "lambda.min" or "lambda.1se", default is "lambda.min.

parallel

Parallelisation

Value

A vector of results for the best Lasso model under the condition where the training set has been used to train and tune, and the testing set is used for obtaining the out of sample prediction error. The return from this function will enter prediction_Lasso.

Details

These are not intended for use by users. This function is one of the main engines for the Lasso 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 no_split_lasso form the overall computation operator for the Lasso models. The return from this function will enter prediction_Lasso for futher wrapping.

Author(s)

Mokyo Zhou


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