marginal_means.brma: Estimated Marginal Means for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

Estimated Marginal Means for brma Objects

Description

Computes estimated marginal means for a fitted brma object with moderators using BayesTools::as_marginal_inference().

Usage

## S3 method for class 'brma'
marginal_means(
  object,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  n_samples = 10000,
  output_measure = NULL,
  transform = NULL,
  bf = NULL,
  ...
)

Arguments

object	a fitted brma object with moderators.
null_hypothesis	point null hypothesis used for inclusion Bayes factors. Defaults to 0.
normal_approximation	whether prior and posterior density at the null should be approximated with a normal distribution. Defaults to FALSE.
n_samples	number of samples/grid points used by BayesTools for marginal prior densities. Defaults to 10000.
output_measure	effect-size measure for location/effect predictions. Defaults to the fitted measure. Supported conversions are among "SMD", "COR", "ZCOR", and "OR"; "RR", "HR", "IRR", "RD", and "GEN" can only be returned on their fitted measure. Use transform = "EXP" for ratio-scale output from log-scale measures.
transform	optional display transformation. Currently "EXP" exponentiates log-scale measures "OR", "RR", "HR", and "IRR".
bf	whether inclusion Bayes factors should be shown by default in summaries. Defaults to TRUE for RoBMA/BMA objects and FALSE for single-model brma objects.
...	additional arguments (currently ignored).

Value

A list of class marginal_means.brma containing the BayesTools marginal_inference object and parameter metadata.

See Also

summary(), plot(), summary.brma(), regplot()

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE) &&
    requireNamespace("metafor", quietly = TRUE)) {
  data(dat.bcg, package = "metadat")
  dat <- metafor::escalc(
    measure = "RR",
    ai      = tpos,
    bi      = tneg,
    ci      = cpos,
    di      = cneg,
    data    = dat.bcg
  )

  fit <- brma(yi = yi, vi = vi, mods = ~ alloc, data = dat, measure = "RR")
  mm  <- marginal_means(fit)
  summary(mm)
  plot(mm, parameter = "alloc")
}

## End(Not run)

RoBMA documentation built on May 7, 2026, 5:08 p.m.