cva.glmnet

Performs k-fold cross-validation for glmnet, including alpha mixing parameter.

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

cva.glmnet function

<dl><dt>x</dt>
<dd>Matrix of predictors</dd>
<dt>y</dt>
<dd>Response vector</dd>
<dt>nfolds</dt>
<dd>Number of folds (default 10)</dd>
<dt>alphaSet</dt>
<dd>Sequence of alpha values to cross-validate</dd>
<dt>foldid</dt>
<dd>Optional vector of values between 1 and <code>nfolds</code> identifying
what fold each observation is in.</dd>
<dt>...</dt>
<dd>Other arguments passed to glmnet::cv.glmnet</dd></dl>

Arguments

Author

Cross-validation of alpha for glmnet — cva.glmnet

<dl>

<dt>x</dt>
<dd>Matrix of predictors</dd>


<dt>y</dt>
<dd>Response vector</dd>


<dt>nfolds</dt>
<dd>Number of folds (default 10)</dd>


<dt>alphaSet</dt>
<dd>Sequence of alpha values to cross-validate</dd>


<dt>foldid</dt>
<dd>Optional vector of values between 1 and <code>nfolds</code> identifying
what fold each observation is in.</dd>


<dt>...</dt>
<dd>Other arguments passed to glmnet::cv.glmnet</dd>

</dl>

cva.glmnet: Cross-validation of alpha for glmnet

Description

Usage

Value

Arguments

Author

See Also