Print Methods for 'anova.rma' and 'list.anova.rma' Objects

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.

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.