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

# S3 method for rma
model_performance(model, metrics = "all", verbose = TRUE, ...)

## Arguments

model 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")). Toggle off warnings. Arguments passed to or from other methods.

## Value

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

## Details

### 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.

## Examples

if (require("metafor")) {
data(dat.bcg)
dat <- escalc(measure = "RR", ai = tpos, bi = tneg, ci = cpos, di = cneg, data = dat.bcg)
model <- rma(yi, vi, data = dat, method = "REML")
model_performance(model)
}
#>