knockoff_filter_mu

Knockoff filter for mu (approximate group control)

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

knockoff_filter_mu function

<dl><dt>data</dt>
<dd>data.frame</dd>
<dt>response</dt>
<dd>response variable name</dd>
<dt>mu_scope</dt>
<dd>RHS-only term labels</dd>
<dt>fdr</dt>
<dd>target FDR level</dd>
<dt>df_smooth</dt>
<dd>df for smoother proxies (splines::bs)</dd></dl>

Arguments

Knockoff filter for mu (approximate group control) — knockoff_filter_mu

<dl>

<dt>data</dt>
<dd>data.frame</dd>


<dt>response</dt>
<dd>response variable name</dd>


<dt>mu_scope</dt>
<dd>RHS-only term labels</dd>


<dt>fdr</dt>
<dd>target FDR level</dd>


<dt>df_smooth</dt>
<dd>df for smoother proxies (splines::bs)</dd>

</dl>

knockoff_filter_mu: Knockoff filter for mu (approximate group control)

Description

Usage

Value

Arguments