post_prob.brma: Posterior Model Probabilities for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

Posterior Model Probabilities for brma Objects

Description

Compute posterior model probabilities from marginal likelihoods of brma models.

Usage

## S3 method for class 'brma'
post_prob(x, ..., prior_prob = NULL, model_names = NULL)

Arguments

x	a brma model object.
...	additional brma model objects.
prior_prob	numeric vector with prior model probabilities or weights. Values must be finite, nonnegative, have the same length as the retained models, and have a positive total. If omitted, a uniform prior is used. Supplied values are normalized internally.
model_names	character vector with model names. If NULL (the default), names will be derived from deparsing the call.

Details

The marginal likelihoods must first be computed using add_marglik. x and at least one additional brma model must be supplied. Non-brma objects in ... are ignored with a warning. All retained models must be fitted to the same outcome target/data, including outcome type and, when present, weights and cluster identifiers.

Value

A named numeric vector with posterior model probabilities (i.e., which sum to one).

See Also

add_marglik, bridge_sampler.brma, bf.brma, logml.brma

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit1 <- brma(yi = yi, vi = vi, data = dat.lehmann2018, measure = "SMD")
  fit2 <- brma(
    yi           = yi,
    vi           = vi,
    data         = dat.lehmann2018,
    measure      = "SMD",
    prior_effect = FALSE
  )

  fit1 <- add_marglik(fit1)
  fit2 <- add_marglik(fit2)

  post_prob(fit1, fit2)
}

## End(Not run)

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