fitted.brma: Fitted Values for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

Fitted Values for brma Objects

Description

Extract in-sample fitted values from a fitted brma object.

Usage

## S3 method for class 'brma'
fitted(
  object,
  unit = "estimate",
  conditioning_depth = "marginal",
  component = "location",
  bias_adjusted = FALSE,
  output_measure = NULL,
  transform = NULL,
  conditional = FALSE,
  ...
)

Arguments

object	a fitted brma object.
unit	output unit. Only "estimate" is implemented currently.
conditioning_depth	conditioning depth for location fitted values. "marginal" uses fixed effects only, "cluster" conditions on cluster-level random effects, and "estimate" conditions on the full estimate-level fitted value.
component	fitted component to return. "location" returns location fitted values, "scale" returns fitted heterogeneity \tau_i, and "all" returns both as a named list.
bias_adjusted	whether location fitted values should adjust for publication bias. Defaults to FALSE.
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".
conditional	whether to return fitted values from conditional posterior predictions for RoBMA product-space objects.
...	additional arguments. Currently only quiet is honored.

Details

This method is a compact adapter around predict.brma. It summarizes posterior prediction draws by their column means and returns a base numeric vector, matching the usual fitted contract. Use predict() directly when posterior draws or intervals are needed.

The default conditioning_depth = "marginal" corresponds to predict(object, type = "terms") and matches the usual fitted-value convention for meta-regression. For normal models, conditioning_depth = "estimate" corresponds to BLUP means for the observed estimates.

For component = "all", conditioning_depth, output_measure, and transform apply only to the location component. The scale component always returns fitted \tau_i values.

Value

A numeric vector of fitted values, or a named list with location and scale components when component = "all".

See Also

predict.brma(), residuals.brma(), blup.brma()

RoBMA documentation built on May 7, 2026, 5:08 p.m.