RoBMA (version 4.0.0)

marginal_means.brma: 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 brma
marginal_means(
  object,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  n_samples = 10000,
  output_measure = NULL,
  transform = NULL,
  bf = NULL,
  ...
)

Value

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

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).

See Also

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

Examples

if (FALSE) {
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")
}
}