plot_posterior

parameter

from

character vector with two values: first, either <code>"mean"</code>
or <code>"50%"</code>; and second, either highest-probability-density interval <code>"hpd"</code>
or the Bayesian credibility interval <code>"bci"</code>.

summary

arguments passed to <code><a rd-options="graphics" href="/link/plot?package=metaBMA&version=0.6.1&to=graphics" data-mini-rdoc="graphics::plot">plot</a></code>

Computes the posterior model probabilities for standard meta-analysis models
(null model vs. alternative model assuming either fixed- or random-effects, respectively).
These posterior probabilities are used to estimate the overall mean effect size
as the weighted average of the mean effect size estimates of the random- and
fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, &
Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define
a wide range of non-informative or informative priors for the mean effect size
and the heterogeneity coefficient. Moreover, using pre-compiled Stan models,
meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS)
priors can be fitted and tested. This allows to compute Bayes factors and
perform Bayesian model averaging across random- and fixed-effects meta-analysis
with and without moderators.

Daniel Heck

metaBMA

Bayesian Model Averaging for Random and Fixed Effects
Meta-Analysis

Daniel W. Heck

Quentin F. Gronau

plot_posterior function

arguments passed to <code><a rd-options='graphics' href='plot'>plot</a></code>

plot_posterior: Plot Posterior Distribution

Description

Usage

Arguments

See Also