sb_selection_frequency

Compute selection frequencies from coefficient paths

Adds variable-selection functions for Beta regression models
(both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm.
Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based
LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector
with an optional standardization speedup, and 'C++' helpers for iterative
re-weighted least squares working steps and precision updates. Also provides
a fastboost_interval() variant for interval responses, comparison helpers,
and a flexible simulator simulation_DATA.beta() for interval-valued data.
For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>.

Frederic Bertrand

SelectBoost.beta

Stability-Selection via Correlated Resampling for
Beta-Regression Models

sb_selection_frequency function

<dl><dt>coef_matrix</dt>
<dd>Matrix produced by <code>sb_apply_selector_manual()</code>.</dd>
<dt>version</dt>
<dd>Either <code>"glmnet"</code> (first row is intercept) or <code>"lars"</code>.</dd>
<dt>threshold</dt>
<dd>Coefficients with absolute value below this threshold are
treated as zero.</dd></dl>

Arguments

Compute selection frequencies from coefficient paths — sb_selection_frequency

<dl>

<dt>coef_matrix</dt>
<dd>Matrix produced by <code>sb_apply_selector_manual()</code>.</dd>


<dt>version</dt>
<dd>Either <code>"glmnet"</code> (first row is intercept) or <code>"lars"</code>.</dd>


<dt>threshold</dt>
<dd>Coefficients with absolute value below this threshold are
treated as zero.</dd>

</dl>

sb_selection_frequency: Compute selection frequencies from coefficient paths

Description

Usage

Value

Arguments