CV_core

<code>CV_core</code> contains the core parts of the cross-validation for Lasso and Post-Lasso.

internal

Provides tools for cross-validated Lasso and Post-Lasso estimation.
Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>,
the main function plasso() extends the standard 'glmnet' output with coefficient paths
for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso
and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) <doi:10.1111/rssa.12623>.

Knaus Michael C.

plasso

Cross-Validated Post-Lasso

Michael C. Knaus

Stefan Glaisner

CV_core function

<dl><dt>x</dt>
<dd>Covariate matrix to be used in cross-validation</dd>
<dt>y</dt>
<dd>Vector of outcomes</dd>
<dt>w</dt>
<dd>Vector of weight</dd>
<dt>cvgroup</dt>
<dd>Categorical with k groups to identify folds</dd>
<dt>list</dt>
<dd>List 1:k</dd>
<dt>i</dt>
<dd>Number of fold that is used for prediction</dd>
<dt>lambda</dt>
<dd>Series of lambdas used</dd>
<dt>...</dt>
<dd>Pass <code>glmnet</code> options</dd></dl>

Arguments

Core part for (Post-) Lasso cross-validation — CV_core

<dl>

<dt>x</dt>
<dd>Covariate matrix to be used in cross-validation</dd>


<dt>y</dt>
<dd>Vector of outcomes</dd>


<dt>w</dt>
<dd>Vector of weight</dd>


<dt>cvgroup</dt>
<dd>Categorical with k groups to identify folds</dd>


<dt>list</dt>
<dd>List 1:k</dd>


<dt>i</dt>
<dd>Number of fold that is used for prediction</dd>


<dt>lambda</dt>
<dd>Series of lambdas used</dd>


<dt>...</dt>
<dd>Pass <code>glmnet</code> options</dd>

</dl>

CV_core: Core part for (Post-) Lasso cross-validation

Description

Usage

Value

Arguments