# bma: Bayesian Model Averaging In metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

## Description

Model averaging for different meta-analysis models (e.g., random-effects or fixed-effects with different priors) based on the posterior model probability.

## Usage

 ```1 2 3 4 5 6 7 8``` ```bma( meta, prior = 1, parameter = "d", summarize = "integrate", ci = 0.95, rel.tol = .Machine\$double.eps^0.5 ) ```

## Arguments

 `meta` list of meta-analysis models (fitted via `meta_random` or `meta_fixed`) `prior` prior probabilities over models (possibly unnormalized). For instance, if the first model is as likely as models 2, 3 and 4 together: `prior = c(3,1,1,1)`. The default is a discrete uniform distribution over models. `parameter` either the mean effect `"d"` or the heterogeneity `"tau"` (i.e., the across-study standard deviation of population effect sizes). `summarize` how to estimate parameter summaries (mean, median, SD, etc.): Either by numerical integration (`summarize = "integrate"`) or based on MCMC/Stan samples (`summarize = "stan"`). `ci` probability for the credibility/highest-density intervals. `rel.tol` relative tolerance used for numerical integration using `integrate`. Use `rel.tol=.Machine\$double.eps` for maximal precision (however, this might be slow).

## Examples

 ```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)) ```

metaBMA documentation built on March 17, 2021, 9:06 a.m.