I2: I2 Heterogeneity Statistic

In donaldRwilliams/blsmeta: Bayesian Location-Scale Meta-Analysis

Description

Compute I2 (total heterogeneity divided by total variability). Note that the I2 formulation for three-level models is described in \insertCitecheung2014modeling;textualblsmeta.

Usage

I2(
  object,
  newdata_scale2 = NULL,
  newdata_scale3 = NULL,
  s2 = NULL,
  cred = 0.95,
  summary = TRUE,
  percent = TRUE,
  digits = 3
)

Arguments

object	An object of class blsmeta.
newdata_scale2	An optional data.frame for which to compute predictions for the level 2 variance component. Defaults to NULL, which then uses the original data used in blsmeta
newdata_scale3	An optional data.frame for which to compute predictions for the level 3 variance component. Defaults to NULL, which then uses the original data used in blsmeta
s2	numeric. A user-defined "typical" sampling variance. Defaults to the estimator in Equation 9 of \insertCiteHiggins2002a;textualblsmeta.
cred	numeric. credible interval (defaults to 0.95).
summary	logical. Should the posterior samples be summarized (defaults to TRUE)?
percent	logical. Should the results be percentages, as in metafor (defaults to TRUE)?
digits	numeric. The desired number of digits for the summarized estimates (defaults to 3).

Value

A data frame of predicted values.

Note

The sampling variances are assumed to be known.

References

\insertAllCited

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Examples

# data
library(psymetadata)

# no scale model
fit <- blsmeta(yi, vi,
               es_id = es_id,
               data = gnambs2020)

# compute I2 for all data
i2 <- I2(fit)

# scale model
fit <- blsmeta(yi, vi,
               es_id = es_id,
               mods_scale2 = ~ n,
               data = gnambs2020)

new_data <- data.frame(n = c(100, 150))

# compute I2 for new data
i2 <- I2(fit, newdata_scale2 = new_data)

donaldRwilliams/blsmeta documentation built on Dec. 20, 2021, 12:12 a.m.