plot_posterior

parameter

from

character vector with two values: first, either <code>"Mean"</code> or <code>"Median"</code>; and second, either highest-probability-density interval (<code>"HPD"</code>) or the quantile interval (<code>"quantile"</code>; function resets margin to default after plotting)

summary

arguments passed to <code><a rd-options="graphics" href="/link/plot?package=metaBMA&version=0.3.9&to=graphics" data-mini-rdoc="graphics::plot">plot</a></code>

Computes the posterior model probabilities for four 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 noninformative or informative priors for the mean effect size
and the heterogeneity coefficient. Funding for this research was provided by
the Berkeley Initiative for Transparency in the Social Sciences, a program of
the Center for Effective Global Action (CEGA), with support from the Laura and
John Arnold Foundation.

Daniel Heck

metaBMA

Bayesian Model Averaging for Random and Fixed Effects
Meta-Analysis

Daniel W. Heck

Quentin F. Gronau

Eric-Jan Wagenmakers

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