predict.cv.grpsel

Generate predictions for new data using specified values of the tuning parameters.

Provides tools for sparse regression modelling with grouped predictors using the group
subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver
near optimal estimates. The group subset penalty can be combined with a group lasso or ridge
penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping
groups.

Ryan Thompson

grpsel

Group Subset Selection

predict.cv.grpsel function

<dl><dt>object</dt>
<dd>an object of class <code>cv.grpsel</code></dd>
<dt>x.new</dt>
<dd>a matrix of new values for the predictors</dd>
<dt>lambda</dt>
<dd>the value of <code>lambda</code> indexing the desired fit</dd>
<dt>gamma</dt>
<dd>the value of <code>gamma</code> indexing the desired fit</dd>
<dt>...</dt>
<dd>any other arguments</dd></dl>

Arguments

Ryan Thompson &lt;ryan.thompson-1@uts.edu.au&gt;

Author

Predict function for cv.grpsel object — predict.cv.grpsel

<dl>

<dt>object</dt>
<dd>an object of class <code>cv.grpsel</code></dd>


<dt>x.new</dt>
<dd>a matrix of new values for the predictors</dd>


<dt>lambda</dt>
<dd>the value of <code>lambda</code> indexing the desired fit</dd>


<dt>gamma</dt>
<dd>the value of <code>gamma</code> indexing the desired fit</dd>


<dt>...</dt>
<dd>any other arguments</dd>

</dl>

predict.cv.grpsel: Predict function for cv.grpsel object

Description

Usage

Value

Arguments

Author