Print method for objects of class "anova.rma".

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

## Author

Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org

## 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

anova.rma for the function to create anova.rma objects.