print.qbrms_p_significance

Print results from probability of practical significance analysis.

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

print.qbrms_p_significance function

<dl><dt>x</dt>
<dd>A <code>qbrms_p_significance</code> object from <code>p_significance()</code>.</dd>
<dt>digits</dt>
<dd>Number of decimal places to display (default 3).</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/print.data.frame()?package=qbrms&version=1.0.1" data-mini-rdoc="qbrms::print.data.frame()">print.data.frame()</a></code>.</dd></dl>

Arguments

Print Method for Enhanced p_significance — print.qbrms_p_significance

<dl>

<dt>x</dt>
<dd>A <code>qbrms_p_significance</code> object from <code>p_significance()</code>.</dd>


<dt>digits</dt>
<dd>Number of decimal places to display (default 3).</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/base/print.dataframe.html'>print.data.frame()</a></code>.</dd>

</dl>

print.qbrms_p_significance: Print Method for Enhanced p_significance

Description

Usage

Value

Arguments