RoBMA (version 4.0.0)

summary_heterogeneity.brma: Summary of Heterogeneity for brma Objects

Description

Computes the absolute heterogeneity (tau, tau^2) and relative measures of heterogeneity (I^2, H^2) for a fitted brma object.

Usage

# S3 method for brma
summary_heterogeneity(object, probs = c(0.025, 0.975), ...)

Value

A list of class summary_heterogeneity.brma containing:

• estimates: A BayesTools_table with heterogeneity statistics

Arguments

object

a fitted brma object

probs

quantiles of the posterior distribution to be displayed. Defaults to c(.025, .975) for 95% credible intervals.

...

additional arguments (currently ignored)

Details

For standard (2-level) random-effects models, the function reports:

• tau: Between-study standard deviation

• tau2: Between-study variance

• I2: Percentage of total variance due to heterogeneity

• H2: Ratio of total to sampling variance

For multilevel (3-level) models with nested effects, the function additionally partitions heterogeneity into estimate-level and cluster-level components:

• rho: Proportion of heterogeneity variance allocated to clusters

• tau [within]: Estimate-level standard deviation

• tau [between]: Cluster-level standard deviation

• tau2 [within]: Estimate-level variance

• tau2 [between]: Cluster-level variance

• I2 [within]: Percentage of variance due to estimate-level heterogeneity

• I2 [between]: Percentage of variance due to cluster-level heterogeneity

For location-scale models, tau2 aggregates the observation-specific heterogeneity variances \(\tau_i^2\); the corresponding tau summary is the square root of this aggregate variance. The relative \(I^2\) and \(H^2\) measures average the observation-specific indices.

The I^2 and H^2 statistics are computed following the metafor package implementation, using the "typical" sampling variance formula from higgins2002quantifying;textualRoBMA. For multilevel models, the partitioned I^2 follows the approach described in the metafor documentation.

References

See Also

pooled_heterogeneity(), summary.brma()

Examples

if (FALSE) {
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- brma(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  summary_heterogeneity(fit)
}
}