predict.cv.glmmsel

Generates predictions for new data using a cross-validated value of the
regularisation parameter.

Provides tools for fitting sparse generalised linear mixed models with l0
regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects
must precede random effects. Uses coordinate descent and local search algorithms to rapidly
deliver near-optimal estimates. Gaussian and binomial response families are currently supported.
For more details see Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.

Ryan Thompson

glmmsel

Generalised Linear Mixed Model Selection

predict.cv.glmmsel function

<dl><dt>object</dt>
<dd>an object of class <code>cv.glmmsel</code></dd>
<dt>x.new</dt>
<dd>a matrix of new values for the predictors</dd>
<dt>cluster.new</dt>
<dd>a vector identifying the clusters that the rows of <code>x.new</code> belong to</dd>
<dt>lambda</dt>
<dd>a value of the regularisation parameter</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.glmmsel object — predict.cv.glmmsel

<dl>

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


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


<dt>cluster.new</dt>
<dd>a vector identifying the clusters that the rows of <code>x.new</code> belong to</dd>


<dt>lambda</dt>
<dd>a value of the regularisation parameter</dd>


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

</dl>

predict.cv.glmmsel: Predict function for `cv.glmmsel` object

Description

Usage

Value

Arguments

Author