conditional_effects

Compute one-dimensional conditional effects / marginal fitted values as a
predictor varies while other covariates are held fixed (typically at means /
modes). Methods should return an object that <code><a href="/link/plot()?package=qbrms&version=1.0.1" data-mini-rdoc="qbrms::plot()">plot()</a></code> can visualise.

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

conditional_effects function

<dl><dt>object</dt>
<dd>A model object.</dd>
<dt>...</dt>
<dd>Passed to methods.</dd></dl>

Arguments

Compute one-dimensional conditional effects / marginal fitted values as a
predictor varies while other covariates are held fixed (typically at means /
modes). Methods should return an object that <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot()</a></code> can visualise.

Conditional effects (generic) — conditional_effects

<dl>

<dt>object</dt>
<dd>A model object.</dd>


<dt>...</dt>
<dd>Passed to methods.</dd>

</dl>

conditional_effects: Conditional effects (generic)

Description

Usage

Value

Arguments