Random Effects for brma Objects

Extracts random effect deviations from a fitted brma object. These are posterior-sample offsets from the fixed-effect predictions, analogous to random-effect deviations returned by metafor::ranef().

Usage

# S3 method for class 'brma'
ranef(object, bias_adjusted = FALSE, probs = c(0.025, 0.975), ...)

Arguments

object

a fitted brma object

bias_adjusted

whether to adjust for publication bias. Defaults to FALSE. See blup.brma for details.

probs

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

...

additional arguments forwarded to predict.brma for supported options such as conditional. newdata, type, quiet, output_measure, and transform are controlled by this method.

Value

For 2-level models, a brma_samples object. For 3-level models, a named list of brma_samples objects (one per variance component).

Details

Random effects are computed as the difference between the true effects (BLUPs) and the fixed-effect predictions: $$u_i = \hat{\theta}_i - \hat{\mu}_i$$

For standard (2-level) models, returns a single brma_samples object with the estimate-level random effects.

For multilevel (3-level) models, returns a list with two observation-aligned brma_samples matrices, one column per estimate row:

cluster

Cluster-level random effects (\(\gamma_j \cdot \tau_{between}\)), representing between-cluster deviations from the fixed effects.

estimate

Estimate-level random effects (\(\theta_i - \mu_i - \gamma_j \cdot \tau_{between}\)), representing within-cluster deviations from the cluster means.

See also

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

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
  )

  ranef(fit)
}
} # }