blup.brma: Best Linear Unbiased Predictions for brma Objects

Description

Computes the estimated true effects (theta) for a fitted brma object. These correspond to Best Linear Unbiased Predictions (BLUPs) or empirical Bayes estimates.

Usage

# S3 method for brma
blup(
  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 a convenience wrapper around predict.brma(..., type = "effect", newdata = NULL).

For unweighted two-level normal models, true effects are computed using empirical Bayes shrinkage: $$\theta_i = \lambda_i \cdot y_i + (1 - \lambda_i) \cdot \mu_i$$ where $\lambda_i = \tau^2 / (\tau^2 + se_i^2)$. With likelihood weights, $se_i^2$ is replaced by the weighted sampling variance $se_i^2 / w_i$.

For GLMM models (binomial, Poisson), the estimate-level random effects are extracted directly from the posterior samples.

For multilevel (3-level) normal models, cluster-level effects are estimated jointly within cluster blocks and estimate-level effects are then shrunk conditional on those cluster effects.

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
  )

  blup(fit)
}
}