COVRATIO for brma Objects

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.

Value

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

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.

See also

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

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)

  covratio(fit)
}
} # }