print.rma.uni: Print and Summary Methods for 'rma' Objects

Description

Print and summary methods for objects of class "rma.uni", "rma.mh", "rma.peto", "rma.glmm", and "rma.glmm".

Usage

## S3 method for class 'rma.uni':
print(x, digits=x$digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"),
      signif.legend=signif.stars, ...)
## S3 method for class 'rma.mh':
print(x, digits=x$digits, showfit=FALSE, \dots)
## S3 method for class 'rma.peto':
print(x, digits=x$digits, showfit=FALSE, \dots)
## S3 method for class 'rma.glmm':
print(x, digits=x$digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"),
      signif.legend=signif.stars, ...)
## S3 method for class 'rma.mv':
print(x, digits=x$digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"),
      signif.legend=signif.stars, ...)
## S3 method for class 'rma':
summary(object, digits=object$digits, showfit=TRUE, \dots)
## S3 method for class 'summary.rma':
print(x, digits=x$digits, showfit=TRUE, signif.stars=getOption("show.signif.stars"),
      signif.legend=signif.stars, ...)

Arguments

an object of class "rma.uni", "rma.mh", "rma.peto", "rma.glmm", "rma.mv", or "summary.rma" (for print).

object

an object of class "rma" (for summary).

digits

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

showfit

logical indicating whether the fit statistics and information criteria should be printed (default is FALSE for print and TRUE for summary).

signif.stars

logical indicating whether p-values should be encoded visually with significance stars. Defaults to the show.signif.stars slot of options.

signif.legend

logical indicating whether the legend for the significance stars should be printed. Defaults to the value for signif.stars.

...

other arguments.

Value

The print functions do not return an object. The summary function returns the object passed to it (with additional class "summary.rma").

Details

The output includes:

the log-likelihood, deviance, AIC, BIC, and AICc value (when settingshowfit=TRUEor by default forsummary).
for objects of class"rma.uni"and"rma.glmm", the amount of (residual) heterogeneity in the random/mixed-effects model (i.e., the estimate of$\tau^2$and its square root). Suppressed for fixed-effects models. The (asymptotic) standard error of the estimate of$\tau^2$is also provided (where possible).
for objects of"rma.mv", a table providing information about the variance components and correlations in the model. For$\sigma^2$components, the estimate and its square root are provided, in addition to the number of values/levels, whether the component was fixed or estimated, and the name of the grouping variable/factor. If theRargument was used to specify known correlation matrices, this is also indicated. For models with an~ inner | outerformula term, the name of the inner and outer grouping variable/factor are given and the number of values/levels of these variables/factors. In addition, for each$\tau^2$component, the estimate and its square root are provided, the number of effects or outcomes observed at each level of the inner grouping variable/factor (only forstruct="HCS"andstruct="UN"), and whether the component was fixed or estimated. Finally, either the estimate of$\rho$(forstruct="CS"orstruct="HCS") or the entire estimated correlation matrix (forstruct="UN") between the levels of the inner grouping variable/factor is provided, again with information whether a particular correlation was fixed or estimated, and how often each combination of levels of the inner grouping variable/factor was observed across the levels of the outer grouping variable/factor.
for objects of class"rma.uni"and"rma.glmm", the$I^2$statistic. For a random-effects model,$I^2$estimates (in percent) how much of the total variability in the effect size estimates (which is composed of heterogeneity plus sampling variability) can be attributed to heterogeneity among the true effects. For a mixed-effects model,$I^2$estimates how much of the unaccounted variability (which is composed of residual heterogeneity plus sampling variability) can be attributed to residual heterogeneity. SeeNotefor how$I^2$is computed.
for objects of class"rma.uni"and"rma.glmm", the$H^2$statistic. For a random-effects model,$H^2$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,$H^2$estimates the ratio of the unaccounted variability in the effect size estimates to the amount of sampling variability. SeeNotefor how$H^2$is computed.
for objects of class"rma.uni", the$R^2$statistic, which estimates the amount of heterogeneity accounted for by the moderators included in the model and can be regarded as a pseudo$R^2$statistic (Raudenbush, 2009). Only provided when fitting a mixed-effects models (i.e., for models including moderators). This is suppressed (and set toNULL) for models without moderators, fixed-effects models, or if the model does not contain an intercept.NAif the amount of heterogeneity is equal to zero to begin with. SeeNotefor how$R^2$is computed.
for objects of class"rma.glmm", the amount of study level variability (only when using a model that models study level differences as a random effect).
the results of the test for (residual) heterogeneity. This is the usual$Q$-test for heterogeneity when not including moderators in the model and the$Q_E$-test for residual heterogeneity when moderators are included. For objects of class"rma.glmm", the results from a Wald-type test and a likelihood ratio test are provided (seerma.glmmfor more details).
the results of the omnibus (Wald-type) test of the coefficients in the model (the indices of the coefficients tested are also indicated). Suppressed if the model includes only one coefficient (e.g., only an intercept, like in the fixed- and random-effects model).
a table with the estimated coefficients, corresponding standard errors, test statistics, p-values, and confidence interval bounds.
the Cochran-Mantel-Haenszel test and Tarone's test for heterogeneity (only when analyzing odds ratios using the Mantel-Haenszel method, i.e.,"rma.mh").

References

Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539--1558. 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. http://www.jstatsoft.org/v36/i03/.