coef.nestcv.glmnet

Extracts coefficients from the final fit of a <code>"nestcv.glmnet"</code> object.

Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.

Myles Lewis

nestedcv

Nested Cross-Validation with 'glmnet' and 'caret'

Athina Spiliopoulou

Cankut Cubuk

David Rios Santini

Katriona Goldmann

Ryan C. Thompson

coef.nestcv.glmnet function

<dl><dt>object</dt>
<dd>Object of class <code>"nestcv.glmnet"</code></dd>
<dt>s</dt>
<dd>Value of penalty parameter lambda. Default is the mean of lambda
values selected across each outer fold.</dd>
<dt>...</dt>
<dd>Other arguments passed to <code>glmnet::coef.glmnet()</code></dd></dl>

Arguments

Extract coefficients from nestcv.glmnet object — coef.nestcv.glmnet

<dl>

<dt>object</dt>
<dd>Object of class <code>"nestcv.glmnet"</code></dd>


<dt>s</dt>
<dd>Value of penalty parameter lambda. Default is the mean of lambda
values selected across each outer fold.</dd>


<dt>...</dt>
<dd>Other arguments passed to <code>glmnet::coef.glmnet()</code></dd>

</dl>

coef.nestcv.glmnet: Extract coefficients from nestcv.glmnet object

Description

Usage

Value

Arguments