diagnose_binomial_mixed

Diagnose potential issues in binomial mixed effects models before fitting

Provides a 'brms'-like interface for fitting Bayesian regression models
using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder).
The package offers faster model fitting while maintaining familiar 'brms' syntax and
output formats. Supports fixed and mixed effects models, multiple probability distributions,
conditional effects plots, and posterior predictive checks with summary methods compatible
with 'brms'. 'TMB' integration provides fast ordinal regression capabilities. Implements
methods adapted from 'emmeans' for marginal means estimation and 'bayestestR' for Bayesian
inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>,
Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>,
Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>,
and Kruschke (2014, ISBN:9780124058880).

Tony Myers

qbrms

Quick Bayesian Regression Models Using 'INLA' with 'brms' Syntax

diagnose_binomial_mixed function

<dl><dt>formula</dt>
<dd>Model formula with mixed effects</dd>
<dt>data</dt>
<dd>Data frame containing variables</dd>
<dt>verbose</dt>
<dd>Logical; print diagnostic information (default: TRUE)</dd></dl>

Arguments

Diagnose Binomial Mixed Effects Models — diagnose_binomial_mixed

<dl>

<dt>formula</dt>
<dd>Model formula with mixed effects</dd>


<dt>data</dt>
<dd>Data frame containing variables</dd>


<dt>verbose</dt>
<dd>Logical; print diagnostic information (default: TRUE)</dd>

</dl>

diagnose_binomial_mixed: Diagnose Binomial Mixed Effects Models

Description

Usage

Value

Arguments