Description Usage Arguments Examples
Model averaging for different metaanalysis models (e.g., randomeffects or fixedeffects with different priors) based on the posterior model probability.
1 2 3 4 5 6 7 8 
meta 
list of metaanalysis models (fitted via

prior 
prior probabilities over models (possibly unnormalized). For instance, if the first model is as likely as models 2, 3 and 4 together: 
parameter 
either the mean effect 
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

1 2 3 4 5 6 7 8 9  # model averaging for fixed and random effects
data(towels)
fixed < meta_fixed(logOR, SE, study, towels)
random < meta_random(logOR, SE, study, towels)
averaged < bma(list("fixed" = fixed, "random" = random))
averaged
plot_posterior(averaged)
plot_forest(averaged, mar = c(4.5, 20, 4, .3))

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