summary_models: Summarize Model-Averaged Component Weights

In RoBMA: Robust Bayesian Meta-Analyses

Summarize Model-Averaged Component Weights

Description

Creates marginal or individual model-weight summaries for RoBMA-class product-space objects.

Usage

summary_models(object, ...)

## S3 method for class 'RoBMA'
summary_models(object, type = "marginal", include_mcmc_diagnostics = TRUE, ...)

## S3 method for class 'summary_models.RoBMA'
print(x, ...)

Arguments

object	a fitted RoBMA-class product-space object, including RoBMA, BMA/BMA.norm, and BMA.glmm.
...	additional arguments
type	whether to summarize marginal component prior distributions ("marginal") or individual model combinations ("individual").
include_mcmc_diagnostics	whether to include Bayes factor MCMC diagnostics in the output. Defaults to TRUE.
x	a summary_models.RoBMA object.

Details

Only mixture-prior components are summarized; non-mixture components are omitted.

Value

A list of class summary_models.RoBMA with elements name and type. For type = "marginal", element marginal contains component tables with columns such as prior_prob, post_prob, and inclusion_BF. For type = "individual", element individual contains individual model combinations and posterior probabilities.

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- RoBMA(yi = yi, vi = vi, data = dat.lehmann2018, measure = "SMD")

  summary_models(fit)
  summary_models(fit, type = "individual")
}

## End(Not run)

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