true_effects.brma: True Effects for brma Objects

Description

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

Usage

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

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().

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.

Details

This function is identical to blup.brma. See that function for full details on how true effects are computed.

Examples

Run this code

if (FALSE) {
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)
}
}