Performance of Meta-Analysis Models
Compute indices of model performance for meta-analysis model from the metafor package.
## S3 method for class 'rma' model_performance(model, metrics = "all", verbose = TRUE, ...)
model |
A |
metrics |
Can be |
verbose |
Toggle off warnings. |
... |
Arguments passed to or from other methods. |
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.
A data frame (with one row) and one column per "index" (see metrics).
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)
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