plot_default

either<code>"psychology"</code> or <code>"medicine"</code>
(uses partial matching, so <code>"p"</code> and "<code>"m"</code> are sufficient)

field

the type of effect size: either
Cohen's d (<code>"d"</code>),
Pearson correlations (<code>"r"</code>),
Fisher's z-transformed correlations (<code>"z"</code>),
or log-odds ratios (<code>"logOR"</code>).

effect

further 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> (e.g., <code>from</code>, <code>to</code>)

Plots default priors for the mean effect \(d\) and the standard deviation of effects \(tau\).

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_default function

further arguments passed to <code><a rd-options='graphics' href='plot'>plot</a></code> (e.g., <code>from</code>, <code>to</code>)

plot_default: Plot Default Priors

Description

Usage

Arguments

See Also

Examples