covratio.brma: COVRATIO for brma Objects

In RoBMA: Robust Bayesian Meta-Analyses

COVRATIO for brma Objects

Description

Computes COVRATIO for a fitted brma object. COVRATIO measures the change in the determinant of the covariance matrix of the estimates when observation i is removed.

Usage

## S3 method for class 'brma'
covratio(model, type = "mods", ...)

Arguments

model	a fitted brma object.
type	type of parameters to be summarized. Defaults to "mods" (for the effect size and meta-regression coefficients). Use "scale" for heterogeneity and scale-regression coefficients.
...	additional arguments. The internal .weights argument can supply precomputed PSIS weights for callers that already extracted them.

Details

COVRATIO is computed using importance sampling weights to approximate the leave-one-out covariance matrices without refitting the model. Estimate-unit LOO must first be computed with model <- add_loo(model, unit = "estimate"), unless internal weights are supplied.

COVRATIO_i = \frac{\det(Cov(\beta)_{-i})}{\det(Cov(\beta))}

Values > 1 indicate that the observation improves precision (decreases variance), while values < 1 indicate that the observation decreases precision (increases variance). If any included parameter has zero posterior variance, or if a full or LOO covariance determinant is zero or non-finite, COVRATIO is undefined. In that case, values are reported as NaN with a printed note when available.

Value

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

See Also

influence.brma, dffits.brma, cooks.distance.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)

  covratio(fit)
}

## End(Not run)

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