check_setup

list of prior distributions for the <code>mu</code> parameter that
will be treated as belonging to the alternative hypothesis. Defaults to <code>
prior(distribution = "normal", parameters = list(mean = 0, sd = 1))</code>.

priors_mu

list of prior distributions for the <code>tau</code> parameter that
will be treated as belonging to the alternative hypothesis. Defaults to <code>
prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15))</code>.

priors_tau

list of prior weight functions for the <code>omega</code>
parameter that will be treated as belonging to the alternative hypothesis.
Defaults to <code>list(
prior(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(.05)), prior_odds = 1/2),
prior(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(.05, .10)), prior_odds = 1/2)
)</code>.

priors_omega

list of prior distributions for the <code>mu</code> parameter that
will be treated as belonging to the null hypothesis. Defaults to point distribution
with location at 0 (
<code>prior(distribution = "point", parameters = list(location = 0))</code>).

priors_mu_null

list of prior distributions for the <code>tau</code> parameter that
will be treated as belonging to the null hypothesis. Defaults to point distribution
with location at 0 (
<code>prior(distribution = "point", parameters = list(location = 0))</code>).

priors_tau_null

list of prior weight functions for the <code>omega</code> parameter
that will be treated as belonging to the null hypothesis. Defaults to point
distribution with location at 1 (
<code>prior(distribution = "point", parameters = list(location = 0))</code>).

priors_omega_null

should the models' details be printed.

models

silent

<code>check_setup</code> prints summary of <code>"RoBMA"</code> ensemble
implied by the specified prior distributions. It is useful for checking
the ensemble configuration prior to fitting all of the models.

A framework for estimating ensembles of meta-analytic models
(assuming either presence or absence of the effect, heterogeneity, and
publication bias) and using Bayesian model averaging to combine them. The
ensembles use Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect) and model-averages
parameter estimates based on posterior model probabilities
(Maier, Barto<c5><a1> & Wagenmakers, 2020, <doi:10.31234/osf.io/u4cns>). The user can
define a wide range of non-informative or informative priors for the
effect size, heterogeneity, and weight functions. The package provides
convenient functions for summary, visualizations, and fit diagnostics.

check_setup: Prints summary of `"RoBMA"` ensemble implied by the specified priors

Description

Usage

Arguments

See Also