plot_alphas

Plot of cross-validated glmnet alpha parameter against deviance for each
outer CV fold.

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

plot_alphas function

<dl><dt>x</dt>
<dd>Fitted "nestcv.glmnet" object</dd>
<dt>col</dt>
<dd>Optional vector of line colours for each fold</dd>
<dt>...</dt>
<dd>other arguments passed to plot</dd></dl>

Arguments

Author

Plot cross-validated glmnet alpha — plot_alphas

<dl>

<dt>x</dt>
<dd>Fitted "nestcv.glmnet" object</dd>


<dt>col</dt>
<dd>Optional vector of line colours for each fold</dd>


<dt>...</dt>
<dd>other arguments passed to plot</dd>

</dl>

plot_alphas: Plot cross-validated glmnet alpha

Description

Usage

Value

Arguments

Author

See Also