cooks.distance.brma: Cook's Distance for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

Cook's Distance for brma Objects

Description

Computes Cook's distance for a fitted brma object. Cook's distance measures the aggregate influence of each observation on the model coefficients.

Usage

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

Arguments

model	a fitted brma object.
...	additional arguments (currently ignored).

Details

Cook's distance is computed as a PSIS leave-one-out deletion diagnostic. For each observation i, normalized PSIS weights estimate the fitted values under the leave-one-out posterior. The distance is the posterior Mahalanobis distance between the full-data and leave-one-out fitted-value vectors:

D_i = \frac{\Delta_i' V_\mu^+ \Delta_i}{P}

where \Delta_i = \hat{\mu} - \hat{\mu}_{(-i)}, V_\mu^+ is the generalized inverse of the full-posterior fitted-value covariance, and P is the rank of the fixed-effect model matrix.

Value

A numeric vector of Cook's distance values, one for each observation.

See Also

influence.brma, dffits.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)

  cooks.distance(fit)
}

## End(Not run)

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