Skip to contents

Summary of Heterogeneity for brma Objects

Source: R/summary_heterogeneity.R
summary_heterogeneity.brma.Rd

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 class 'brma'
summary_heterogeneity(object, probs = c(0.025, 0.975), ...)

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)

Value

A list of class summary_heterogeneity.brma containing:

  • estimates: A BayesTools_table with heterogeneity statistics

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 Higgins and Thompson (2002) . For multilevel models, the partitioned I^2 follows the approach described in the metafor documentation.

References

Higgins JP, Thompson SG (2002). “Quantifying heterogeneity in a meta-analysis.” Statistics in Medicine, 21(11), 1539–1558. doi:10.1002/sim.1186 .

See also

pooled_heterogeneity(), summary.brma()

Examples

if (FALSE) { # \dontrun{
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)
}
} # }