model_performance.rma {performance} | R Documentation |
Compute indices of model performance for meta-analysis model from the metafor package.
## S3 method for class 'rma' model_performance( model, metrics = "all", estimator = "ML", verbose = TRUE, ... )
model |
A |
metrics |
Can be |
estimator |
Only for linear models. Corresponds to the different
estimators for the standard deviation of the errors. If |
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) }