Computes hat values (leverages) from a fitted brma object. Returns posterior mean leverages, one for each observation.
Usage
# S3 method for class 'brma'
hatvalues(model, ...)Details
Hat values (leverages) measure the influence of each observation on the fitted values. In a Bayesian meta-analysis, the random effects variance \(\tau^2\) is uncertain, so the hat matrix depends on the posterior samples of \(\tau^2\).
This function computes the diagonal elements of the hat matrix: $$h_i = (X (X^T W X)^{-1} X^T W)_{ii}$$ where \(W\) is the weight matrix inverse to the marginal variance matrix.
The hat matrix is computed for each posterior draw and then averaged over
draws, matching the vector output shape used by metafor.
This method is available only for normal outcome models without weightfunction selection.
Examples
if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE) &&
requireNamespace("metafor", quietly = TRUE)) {
data(dat.bcg, package = "metadat")
dat <- metafor::escalc(
measure = "RR",
ai = tpos,
bi = tneg,
ci = cpos,
di = cneg,
data = dat.bcg
)
fit <- brma(yi = yi, vi = vi, data = dat, measure = "RR")
hatvalues(fit)
}
} # }