print.RoBMA_data

Prints a concise summary of an RoBMA_data object.

A framework for Bayesian meta-analysis, including model estimation,
prior specification, model comparison, prediction, summaries, visualizations,
and diagnostics. The package fits single and model-averaged meta-analytic,
meta-regression, multilevel, publication bias adjusted, and generalized linear
mixed models The model-averaged meta-analytic models combine competing models
based on their predictive performance, weight inference by posterior model probabilities,
and test model components using Bayes factors (e.g., effect vs. no effect;
Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
<doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>).
Users can specify flexible prior distributions for effect sizes, heterogeneity,
publication bias (including selection models and PET-PEESE), and moderators.

Franti<c5><a1>ek Barto<c5><a1>

RoBMA

Robust Bayesian Meta-Analyses

František Bartoš

Maximilian Maier

Eric-Jan Wagenmakers

Joris Goosen

Matthew Denwood

Martyn Plummer

print.RoBMA_data function

<dl><dt>x</dt>
<dd>an RoBMA_data object</dd>
<dt>n</dt>
<dd>maximum number of rows to display for each component. Defaults to 6.</dd>
<dt>...</dt>
<dd>additional arguments (ignored)</dd></dl>

Arguments

Print method for RoBMA_data objects — print.RoBMA_data

<dl>

<dt>x</dt>
<dd>an RoBMA_data object</dd>


<dt>n</dt>
<dd>maximum number of rows to display for each component. Defaults to 6.</dd>


<dt>...</dt>
<dd>additional arguments (ignored)</dd>

</dl>

print.RoBMA_data: Print method for RoBMA_data objects

Description

Usage

Value

Arguments