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, ...)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.
Examples
if (FALSE) { # \dontrun{
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)
}
} # }