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Compute indices of model performance for meta-analysis model from the metafor package.


# S3 method for class 'rma'
  metrics = "all",
  estimator = "ML",
  verbose = TRUE,



A rma object as returned by metafor::rma().


Can be "all" or a character vector of metrics to be computed (some of c("AIC", "BIC", "I2", "H2", "TAU2", "R2", "CochransQ", "QE", "Omnibus", "QM")).


Only for linear models. Corresponds to the different estimators for the standard deviation of the errors. If estimator = "ML" (default, except for performance_aic() when the model object is of class lmerMod), the scaling is done by n (the biased ML estimator), which is then equivalent to using AIC(logLik()). Setting it to "REML" will give the same results as AIC(logLik(..., REML = TRUE)).


Toggle off warnings.


Arguments passed to or from other methods.


A data frame (with one row) and one column per "index" (see metrics).


Indices of fit

  • AIC Akaike's Information Criterion, see ?stats::AIC

  • BIC Bayesian Information Criterion, see ?stats::BIC

  • I2: For a random effects model, I2 estimates (in percent) how much of the total variability in the effect size estimates can be attributed to heterogeneity among the true effects. For a mixed-effects model, I2 estimates how much of the unaccounted variability can be attributed to residual heterogeneity.

  • H2: For a random-effects model, H2 estimates the ratio of the total amount of variability in the effect size estimates to the amount of sampling variability. For a mixed-effects model, H2 estimates the ratio of the unaccounted variability in the effect size estimates to the amount of sampling variability.

  • TAU2: The amount of (residual) heterogeneity in the random or mixed effects model.

  • CochransQ (QE): Test for (residual) Heterogeneity. Without moderators in the model, this is simply Cochran's Q-test.

  • Omnibus (QM): Omnibus test of parameters.

  • R2: Pseudo-R2-statistic, which indicates the amount of heterogeneity accounted for by the moderators included in a fixed-effects model.

See the documentation for ?metafor::fitstats.


data(, package = "metadat")
dat <- metafor::escalc(
  measure = "RR",
  ai = tpos,
  bi = tneg,
  ci = cpos,
  di = cneg,
  data =
model <- metafor::rma(yi, vi, data = dat, method = "REML")
#> # Indices of model performance
#> AIC    |    BIC |    I2 |     H2 |  TAU2 | CochransQ | p (CochransQ) | df | Omnibus | p (Omnibus)
#> -------------------------------------------------------------------------------------------------
#> 29.376 | 30.345 | 0.922 | 12.856 | 0.313 |   152.233 |        < .001 | 12 |  15.796 |      < .001