Functions to print objects of class `"anova.rma"`

and `"list.anova.rma"`

.

```
# S3 method for class 'anova.rma'
print(x, digits=x$digits, ...)
# S3 method for class 'list.anova.rma'
print(x, digits=x[[1]]$digits, ...)
```

## Arguments

- x
an object of class `"anova.rma"`

or `"list.anova.rma"`

obtained with `anova`

.

- digits
integer to specify the number of decimal places to which the printed results should be rounded (the default is to take the value from the object).

- ...
other arguments.

## Details

For a Wald-type test of one or multiple model coefficients, the output includes the test statistic (either a chi-square or F-value) and the corresponding p-value.

When testing one or multiple contrasts, the output includes the estimated value of the contrast, its standard error, test statistic (either a z- or a t-value), and the corresponding p-value.

When comparing two model objects, the output includes:

the number of parameters in the full and the reduced model.

the AIC, BIC, AICc, and log-likelihood of the full and the reduced model.

the value of the likelihood ratio test statistic.

the corresponding p-value.

the test statistic of the test for (residual) heterogeneity for the full and the reduced model.

the estimate of \(\tau^2\) from the full and the reduced model. Suppressed for equal-effects models.

amount (in percent) of heterogeneity in the reduced model that is accounted for in the full model (`NA`

for `"rma.mv"`

objects). This can be regarded as a pseudo \(R^2\) statistic (Raudenbush, 2009). Note that the value may not be very accurate unless \(k\) is large (Lopez-Lopez et al., 2014).

The last two items are not provided when comparing `"rma.mv"`

models.

## Value

The function does not return an object.

## References

López-López, J. A., Marín-Martínez, F., Sánchez-Meca, J., Van den Noortgate, W., & Viechtbauer, W. (2014). Estimation of the predictive power of the model in mixed-effects meta-regression: A simulation study. *British Journal of Mathematical and Statistical Psychology*, **67**(1), 30–48. https://doi.org/10.1111/bmsp.12002

Raudenbush, S. W. (2009). Analyzing effect sizes: Random effects models. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), *The handbook of research synthesis and meta-analysis* (2nd ed., pp. 295–315). New York: Russell Sage Foundation.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. *Journal of Statistical Software*, **36**(3), 1–48. https://doi.org/10.18637/jss.v036.i03

## See also

`anova`

for the function to create `anova.rma`

objects.