bselmodel: Bayesian Selection Model

In RoBMA: Robust Bayesian Meta-Analyses

Bayesian Selection Model

Description

Function for fitting random-effects, meta-regression, multilevel, and location-scale meta-analytic selection models.

Usage

bselmodel(
  yi,
  vi,
  sei,
  weights,
  ni,
  mods,
  scale,
  cluster,
  data,
  slab,
  subset,
  measure,
  prior_effect,
  prior_heterogeneity,
  prior_mods,
  prior_scale,
  prior_heterogeneity_allocation,
  prior_bias,
  standardize_continuous_predictors = TRUE,
  set_contrast_factor_predictors = "treatment",
  prior_unit_information_sd,
  rescale_priors = 1,
  prior_informed_field,
  prior_informed_subfield,
  effect_direction = "detect",
  steps,
  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,
  ...
)

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".
prior_effect	prior distribution for the effect size (\mu) parameter (the intercept). If omitted, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.
prior_heterogeneity	prior distribution for the heterogeneity (\tau) parameter. If omitted, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.
prior_mods	prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used.
prior_scale	prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used.
prior_heterogeneity_allocation	prior distribution for the fraction of heterogeneity allocated to the cluster-level component in multilevel models (\rho). If omitted or NULL, defaults to Beta(1, 1).
prior_bias	selection-model bias prior, usually created by prior_weightfunction(). If omitted or NULL, a default one-sided weightfunction prior is constructed from steps.
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.
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.
steps	numeric vector of one-sided p-value cut points for the default selection model. If prior_bias is supplied, the prior carries its own side, steps, and weights. If omitted, the default is 0.025, yielding intervals ⁠[0, .025]⁠ and ⁠(.025, 1]⁠.
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

bselmodel() is a normal/effect-size selection-model constructor. Custom prior_bias can be a weightfunction prior or a supported BayesTools selection-kernel prior; p-hacking kernels are not supported in active RoBMA.

Value

A fitted object of class c("bselmodel", "brma") containing a single Bayesian selection model fit.

See Also

publication_bias_prior_specification, RoBMA(), bPET(), bPEESE(), summary.brma(), funnel.brma()

Examples

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

  fit <- bselmodel(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    steps   = 0.025,
    seed    = 1,
    silent  = TRUE
  )

  summary(fit)
  funnel(fit)
}

## End(Not run)

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