performance (version 0.4.5)

model_performance.rma: Performance of Meta-Analysis Models

Description

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

Usage

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

Arguments

model

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

metrics

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

verbose

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 AIC

• BIC Bayesian Information Criterion, see 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.

• 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 fitstats.

Examples

# NOT RUN {
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)
}
# }