RoBMA: Robust Bayesian Model-Averaged Meta-Analysis

In RoBMA: Robust Bayesian Meta-Analyses

Robust Bayesian Model-Averaged Meta-Analysis

Description

Fits a robust Bayesian model-averaged meta-analysis. The default ensemble averages across models with and without an effect, heterogeneity, and publication-bias adjustment.

Usage

RoBMA(
  yi,
  vi,
  sei,
  weights,
  ni,
  mods,
  scale,
  cluster,
  data,
  slab,
  subset,
  measure,
  effect_direction = "detect",
  prior_effect,
  prior_heterogeneity,
  prior_mods,
  prior_scale,
  prior_heterogeneity_allocation,
  prior_bias,
  prior_effect_null,
  prior_heterogeneity_null,
  prior_mods_null,
  prior_scale_null,
  prior_heterogeneity_allocation_null,
  prior_bias_null,
  standardize_continuous_predictors = TRUE,
  set_contrast_factor_predictors = "meandif",
  prior_unit_information_sd,
  rescale_priors = 1,
  prior_informed_field,
  prior_informed_subfield,
  model_type = "PSMA",
  sample = 5000,
  burnin = 2000,
  adapt = 500,
  chains = 3,
  thin = 1,
  parallel = FALSE,
  autofit = FALSE,
  autofit_control = set_autofit_control(),
  convergence_checks = set_convergence_checks(),
  seed = NULL,
  silent = TRUE,
  ...
)

Arguments

yi	a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.
vi	a vector of sampling variances. Either vi or sei must be supplied for normal models.
sei	a vector of standard errors. Either vi or sei must be supplied for normal models.
weights	an optional vector of positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study.
ni	an optional vector of sample sizes. Used for measure = "GEN" or when estimating "UISD").
mods	an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data.
scale	an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data.
cluster	an optional vector of cluster identifiers for multilevel meta-analysis.
data	an optional data frame containing the variables.
slab	an optional vector of study labels.
subset	an optional logical or numeric vector specifying a subset of data to be used.
measure	a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".
effect_direction	direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.
prior_effect	prior distribution(s) for the alternative effect component(s).
prior_heterogeneity	prior distribution(s) for the alternative heterogeneity component(s).
prior_mods	prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components.
prior_scale	prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components.
prior_heterogeneity_allocation	prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s).
prior_bias	prior distribution(s) for alternative publication-bias component(s), such as weight functions, PET, or PEESE. Alternative prior arguments can be: A single prior distribution object (creates a mixture with one alternative) A list of prior distributions (creates a mixture with multiple alternatives) NULL or FALSE (omits the alternative hypothesis component) See publication_bias_prior_specification for details on specifying publication-bias priors and prior_specification for details on specifying meta-analytic parameter priors.
prior_effect_null	prior distribution(s) for the null effect component(s).
prior_heterogeneity_null	prior distribution(s) for the null heterogeneity component(s).
prior_mods_null	prior distribution(s) for null moderator components. A single prior applies to all terms; a named list can specify term-specific components.
prior_scale_null	prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components.
prior_heterogeneity_allocation_null	prior distribution(s) for the null cluster-level heterogeneity allocation component(s).
prior_bias_null	prior distribution(s) for null publication-bias component(s), usually prior_none(). See publication_bias_prior_specification. Null prior arguments can be: A single prior distribution object (creates a mixture with one null) A list of prior distributions (creates a mixture with multiple nulls) NULL or FALSE (omits the null hypothesis component) Defaults to a point mass (spike) at zero for effect and heterogeneity parameters.
standardize_continuous_predictors	logical. Whether to standardize continuous predictors. Defaults to TRUE.
set_contrast_factor_predictors	character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage; single-model constructors use "treatment", while model-averaging constructors use "meandif".
prior_unit_information_sd	numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.
rescale_priors	numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged. For constructors with publication-bias prior distributions, rescale_priors does not rescale them except for the default PEESE prior's UISD adjustment. Defaults to 1.
prior_informed_field	character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.
prior_informed_subfield	character. The subfield of the informed prior distributions. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.
model_type	character string specifying predefined publication-bias model ensembles. One of: "PSMA" (default): Full RoBMA-PSMA ensemble with 6 weight functions + PET + PEESE "6w": Six weight function models "2w": Two weight function models "PP": PET-PEESE models only Custom prior_bias replaces the preset alternative bias components. If prior_bias is omitted, model_type determines the default alternatives even when prior_bias_null is customized or omitted.
sample	numeric. Number of MCMC samples to save. Defaults to 5000.
burnin	numeric. Number of burn-in iterations. Defaults to 2000.
adapt	numeric. Number of adaptation iterations. Defaults to 500.
chains	numeric. Number of MCMC chains. Defaults to 3.
thin	numeric. Thinning interval. Defaults to 1.
parallel	logical. Whether to run MCMC chains in parallel. Defaults to FALSE.
autofit	logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.
autofit_control	list of autofit control settings. See set_autofit_control() for details.
convergence_checks	list of convergence check settings. See set_convergence_checks() for details.
seed	numeric. Random seed for reproducibility. Defaults to NULL.
silent	logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.
...	additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

Details

RoBMA() uses product-space Bayesian model averaging. Inclusion Bayes factors and model-averaged estimates are obtained from mixture priors for effect, heterogeneity, moderators, scale regression, and publication-bias components.

By default, model_type = "PSMA" includes selection-model weight functions together with PET and PEESE publication-bias adjustments. Use BMA() for model averaging without publication-bias adjustment, or brma() for fitting a single meta-analytic model.

RoBMA() uses normal/effect-size input (yi with vi or sei). Raw-count GLMM model averaging is provided by BMA.glmm().

Product-space objects support predictive comparison with add_loo() and add_waic(). Bridge-sampling marginal likelihood via add_marglik() is not available for product-space model-averaging objects.

Value

A fitted object of class c("RoBMA", "brma"). The object contains checked data, checked priors, the JAGS fit, cached summary, and cached coefficients. It can be passed to summary(), plot(), predict(), funnel(), add_loo(), and related methods.

See Also

publication_bias_prior_specification, BMA(), brma(), bselmodel(), bPET(), bPEESE(), summary.brma(), plot.brma()

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")

  fit <- RoBMA(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  summary(fit)
  plot(fit)
}

## End(Not run)

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