predicted_bf

How much can be learned by an additional study? To judge this, this function
samples the distribution of predicted Bayes factors for a new study given the
current evidence.

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. For a primer on Bayesian model-averaged meta-analysis,
see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).

Daniel W. Heck

metaBMA

Bayesian Model Averaging for Random and Fixed Effects
Meta-Analysis

Quentin F. Gronau

Eric-Jan Wagenmakers

Indrajeet Patil

predicted_bf function

<dl><dt>meta</dt>
<dd>model-averaged meta-analysis (fitted with <code>meta_bma</code>).</dd>
<dt>SE</dt>
<dd>a scalar: the expected standard error of future study.
For instance, SE = 1/sqrt(N) for standardized effect sizes and N = sample size)</dd>
<dt>sample</dt>
<dd>number of simulated Bayes factors</dd>
<dt>...</dt>
<dd>further arguments passed to rstan::sampling to draw posterior samples for d and tau.</dd></dl>

Arguments

Predicted Bayes Factors for a New Study — predicted_bf

<dl>

<dt>meta</dt>
<dd>model-averaged meta-analysis (fitted with <code>meta_bma</code>).</dd>


<dt>SE</dt>
<dd>a scalar: the expected standard error of future study.
For instance, SE = 1/sqrt(N) for standardized effect sizes and N = sample size)</dd>


<dt>sample</dt>
<dd>number of simulated Bayes factors</dd>


<dt>...</dt>
<dd>further arguments passed to rstan::sampling to draw posterior samples for d and tau.</dd>

</dl>

predicted_bf: Predicted Bayes Factors for a New Study

Description

Usage

Arguments