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Fitted Values for brma Objects

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

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.

Value

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

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.

See also

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