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

`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", "CochransQ", "QE", "Omnibus", "QM")`

).- estimator
Only for linear models. Corresponds to the different estimators for the standard deviation of the errors. If

`estimator = "ML"`

(default, except for`performance_aic()`

when the model object is of class`lmerMod`

), the scaling is done by`n`

(the biased ML estimator), which is then equivalent to using`AIC(logLik())`

. Setting it to`"REML"`

will give the same results as`AIC(logLik(..., REML = TRUE))`

.- 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`

.

## 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)
#> # Indices of model performance
#>
#> AIC | BIC | I2 | H2 | TAU2 | CochransQ | p (CochransQ) | df | Omnibus | p (Omnibus)
#> -------------------------------------------------------------------------------------------------
#> 29.376 | 30.345 | 0.922 | 12.856 | 0.313 | 152.233 | < .001 | 12 | 15.796 | < .001
```