model_performance.rma {performance} | R Documentation |
Performance of Meta-Analysis Models
Description
Compute indices of model performance for meta-analysis model from the metafor package.
Usage
## S3 method for class 'rma'
model_performance(
model,
metrics = "all",
estimator = "ML",
verbose = TRUE,
...
)
Arguments
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. |
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
.
Value
A data frame (with one row) and one column per "index" (see
metrics
).
Examples
data(dat.bcg, package = "metadat")
dat <- metafor::escalc(
measure = "RR",
ai = tpos,
bi = tneg,
ci = cpos,
di = cneg,
data = dat.bcg
)
model <- metafor::rma(yi, vi, data = dat, method = "REML")
model_performance(model)