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Pooled Effect Size for brma Objects

Source: R/wrappers.R
pooled_effect.brma.Rd

Computes the pooled (aggregated) effect size estimate from a fitted brma object by averaging across the moderator model matrix.

Usage

# S3 method for class 'brma'
pooled_effect(
  object,
  bias_adjusted = TRUE,
  output_measure = NULL,
  transform = NULL,
  probs = c(0.025, 0.975),
  conditional = FALSE,
  ...
)

Arguments

object

a fitted brma object

bias_adjusted

whether to adjust for publication bias. Defaults to TRUE. For PET/PEESE models this removes the regression bias term from the pooled location effect. Selection-model weighting affects response predictions, not this type = "terms" wrapper.

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".

probs

quantiles of the posterior distribution to be displayed. Defaults to c(.025, .975) for 95% credible intervals.

conditional

whether to return the pooled effect conditional on the effect component for RoBMA product-space objects. Defaults to FALSE.

...

additional arguments passed to predict.brma; wrapper arguments such as newdata, type, and quiet are fixed.

Value

A brma_samples object containing posterior samples. When printed, displays a summary table. Use summary() to obtain the summary table directly. The samples can be converted to posterior draws formats using as_draws().

Details

This function is a convenience wrapper around predict.brma(..., type = "terms", newdata = TRUE, bias_adjusted = TRUE, quiet = TRUE).

For meta-regression models, the pooled effect averages the effect size estimate across moderator levels proportionately to the levels observed in the data. This provides an estimate representative of the sample of studies.

For models without moderators, this returns the single mu parameter.

See also

predict.brma(), pooled_heterogeneity(), blup()

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- brma(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  pooled_effect(fit)
}
} # }