fast_vs_generic_ll

Compare fast vs generic deviance log-likelihood evaluation

Extends the 'SelectBoost' approach to Generalized Additive Models for
Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection
across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC().
Includes optional standardization of predictors and helper functions for
corrected AIC calculation. More details can be found in Bertrand and Maumy (2024)
<https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve
variable selection for GAMLSS and quantile regression when predictors are
numerous and highly correlated.

Frederic Bertrand

SelectBoost.gamlss

Stability-Selection via Correlated Resampling for 'GAMLSS'
Models

fast_vs_generic_ll function

<dl><dt>fit</dt>
<dd>A <code>gamlss</code> fit.</dd>
<dt>newdata</dt>
<dd>Data frame to evaluate on.</dd>
<dt>reps</dt>
<dd>Number of repetitions (default 100).</dd>
<dt>unit</dt>
<dd>microbenchmark unit (default "us").</dd></dl>

Arguments

Compare fast vs generic deviance log-likelihood evaluation — fast_vs_generic_ll

<dl>

<dt>fit</dt>
<dd>A <code>gamlss</code> fit.</dd>


<dt>newdata</dt>
<dd>Data frame to evaluate on.</dd>


<dt>reps</dt>
<dd>Number of repetitions (default 100).</dd>


<dt>unit</dt>
<dd>microbenchmark unit (default "us").</dd>

</dl>

fast_vs_generic_ll: Compare fast vs generic deviance log-likelihood evaluation

Description

Usage

Value

Arguments