Description Usage Arguments Examples
Runs a Bayesian metaanalysis assuming that the mean effect d in each study is identical (i.e., a fixedeffects analysis).
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y 
effect size per study. Can be provided as (1) a numeric vector, (2)
the quoted or unquoted name of the variable in 
SE 
standard error of effect size for each study. Can be a numeric
vector or the quoted or unquoted name of the variable in 
labels 
optional: character values with study labels. Can be a
character vector or the quoted or unquoted name of the variable in

data 
data frame containing the variables for effect size 
d 

rscale_contin 
scale parameter of the JZS prior for the continuous covariates. 
rscale_discrete 
scale parameter of the JZS prior for discrete moderators. 
centering 
whether continuous moderators are centered. 
logml 
how to estimate the logmarginal likelihood: either by numerical
integration ( 
summarize 
how to estimate parameter summaries (mean, median, SD,
etc.): Either by numerical integration ( 
ci 
probability for the credibility/highestdensity intervals. 
rel.tol 
relative tolerance used for numerical integration using

silent_stan 
whether to suppress the Stan progress bar. 
... 
further arguments passed to 
1 2 3 4 5 6 7 8 9  ### Bayesian FixedEffects MetaAnalysis (H1: d>0)
data(towels)
mf < meta_fixed(logOR, SE, study,
data = towels,
d = prior("norm", c(mean = 0, sd = .3), lower = 0)
)
mf
plot_posterior(mf)
plot_forest(mf)

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