predict.cva.glmnet

Makes predictions from a cross-validated glmnet model with optimal value of
lambda and alpha.

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

predict.cva.glmnet function

<dl><dt>object</dt>
<dd>Fitted <code>cva.glmnet</code> object.</dd>
<dt>newx</dt>
<dd>Matrix of new values for <code>x</code> at which predictions are to be made.</dd>
<dt>s</dt>
<dd>Value of penalty parameter lambda. Default value is <code>s="lambda.1se"</code>
for consistency with glmnet. Alternatively <code>s="lambda.min"</code> can be used.</dd>
<dt>...</dt>
<dd>Other arguments passed to <code>predict.cv.glmnet()</code>.</dd></dl>

Arguments

Predict method for cva.glmnet models — predict.cva.glmnet

<dl>

<dt>object</dt>
<dd>Fitted <code>cva.glmnet</code> object.</dd>


<dt>newx</dt>
<dd>Matrix of new values for <code>x</code> at which predictions are to be made.</dd>


<dt>s</dt>
<dd>Value of penalty parameter lambda. Default value is <code>s="lambda.1se"</code>
for consistency with glmnet. Alternatively <code>s="lambda.min"</code> can be used.</dd>


<dt>...</dt>
<dd>Other arguments passed to <code>predict.cv.glmnet()</code>.</dd>

</dl>

predict.cva.glmnet: Predict method for cva.glmnet models

Description

Usage

Value

Arguments