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True Effects for brma Objects

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

Computes the estimated true effects (theta) for a fitted brma object. This is an alias for blup.brma.

Usage

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

Arguments

object

a fitted brma object

bias_adjusted

whether to adjust for publication bias. Defaults to FALSE, which returns estimates including publication bias effects (i.e., what we expect the true effects to be given the biased observations). Set to TRUE to obtain bias-corrected estimates.

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.

...

additional arguments passed to predict.brma; wrapper arguments such as newdata, type, quiet, output_measure, and transform are fixed by this method.

Value

A brma_samples object containing posterior draws of BLUP or empirical-Bayes true-effect summaries with one column per estimate. For existing normal data, these are conditional BLUP means, not simulated latent-effect draws. 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 identical to blup.brma. See that function for full details on how true effects are computed.

See also

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

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
  )

  true_effects(fit)
}
} # }