plot.meta_pred

an objct of the class <code>"prediction"</code> which contains observed and predicted Bayes factors

a character value defining which Bayes factor to plot (one of "d_10_fixed", "d_10_random", "d_10_averaged", "tau_10_random", "H1_fixed_vs_random")

which

either plots Bayes factors (<code>"BF"</code>), inverse Bayes factors (<code>"1/BF"</code>), log Bayes factors (<code>"log"</code>), or the log-inverse Bayes factor (<code>"1/log"</code>)

scale

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.meta_pred function

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

plot.meta_pred: Plot Predicted Bayes Factors

Description

Usage

Arguments