dffits.brma: DFFITS for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

DFFITS for brma Objects

Description

Computes DFFITS (Difference in FITS, standardized) for a fitted brma object. DFFITS measures how much the fitted value for observation i changes if observation i is removed, standardized by the estimated standard error of the fit.

Usage

## S3 method for class 'brma'
dffits(model, ...)

Arguments

model	a fitted normal-outcome brma object without a weightfunction component.
...	additional arguments (currently ignored).

Details

DFFITS values are computed as a PSIS leave-one-out deletion diagnostic. For each observation i, the leave-one-out posterior mean fitted value at that observation is estimated with normalized PSIS weights and compared to the full-posterior fitted value:

DFFITS_i = \frac{\hat{\mu}_i - \hat{\mu}_{i(-i)}}{SD_{(-i)}(\mu_i)}

This targets deletion influence on fitted values directly. It does not use LOO-PIT residuals, which are predictive outlier diagnostics rather than fitted-value deletion diagnostics.

Estimate-unit LOO must first be computed with model <- add_loo(model, unit = "estimate"). If the leave-one-out posterior SD of a fitted value is near zero, the corresponding DFFITS value is returned as NA.

Value

A named numeric vector of DFFITS values, one for each observation.

See Also

influence.brma, cooks.distance.brma, hatvalues.brma

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- bPET(yi = yi, vi = vi, data = dat.lehmann2018, measure = "SMD")
  fit <- add_loo(fit)

  dffits(fit)
}

## End(Not run)

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