.detect_separation

Detects complete or quasi-separation in logistic-type models,
which can cause boundary issues in parameter estimation.

internal

Calculates risk differences (or prevalence differences for
cross-sectional data) using generalized linear models with automatic
link function selection. Provides robust model fitting with fallback
methods, support for stratification and adjustment variables, inverse
probability of treatment weighting (IPTW) for causal inference, and
publication-ready output formatting. Handles model convergence issues
gracefully and provides confidence intervals using multiple approaches.
Methods are based on approaches described in Mark W. Donoghoe and
Ian C. Marschner (2018) "logbin: An R Package for Relative Risk
Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09>
for robust GLM fitting, Peter C. Austin (2011) "An Introduction to
Propensity Score Methods for Reducing the Effects of Confounding in
Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods,
and standard epidemiological methods for risk difference estimation as
described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash
(2008, ISBN:9780781755641) "Modern Epidemiology".

John D. Murphy

riskdiff

Risk Difference Estimation with Multiple Link Functions and
Inverse Probability of Treatment Weighting

.detect_separation function

<dl><dt>model</dt>
<dd>A fitted GLM object</dd>
<dt>data</dt>
<dd>The data used to fit the model</dd>
<dt>verbose</dt>
<dd>Logical for diagnostic output</dd></dl>

Arguments

Detect Complete or Quasi-Separation — .detect_separation

<dl>

<dt>model</dt>
<dd>A fitted GLM object</dd>


<dt>data</dt>
<dd>The data used to fit the model</dd>


<dt>verbose</dt>
<dd>Logical for diagnostic output</dd>

</dl>

.detect_separation: Detect Complete or Quasi-Separation

Description

Usage

Value

Arguments