"rma.uni"
, "rma.mh"
, "rma.peto"
, "rma.glmm"
, and "rma.glmm"
.
"print"(x, digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"), signif.legend=signif.stars, ...)
"print"(x, digits, showfit=FALSE, ...)
"print"(x, digits, showfit=FALSE, ...)
"print"(x, digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"), signif.legend=signif.stars, ...)
"print"(x, digits, showfit=FALSE, signif.stars=getOption("show.signif.stars"), signif.legend=signif.stars, ...)
"summary"(object, digits, showfit=TRUE, ...)
"print"(x, digits, showfit=TRUE, signif.stars=getOption("show.signif.stars"), signif.legend=signif.stars, ...)
"rma.uni"
, "rma.mh"
, "rma.peto"
, "rma.glmm"
, "rma.mv"
, or "summary.rma"
(for print
)."rma"
(for summary
).FALSE
for print
and TRUE
for summary
).show.signif.stars
slot of options
.signif.stars
.print
functions do not return an object. The summary
function returns the object passed to it (with additional class "summary.rma"
).
showfit=TRUE
or by default for summary
).
"rma.uni"
and "rma.glmm"
, the amount of (residual) heterogeneity in the random/mixed-effects model (i.e., the estimate of \tau² and its square root). Suppressed for fixed-effects models. The (asymptotic) standard error of the estimate of \tau² is also provided (where possible).
"rma.mv"
, a table providing information about the variance components and correlations in the model. For \sigma² 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 the R
argument was used to specify known correlation matrices, this is also indicated. For models with an ~ inner | outer
formula 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 \tau² 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 for struct="HCS"
, struct="DIAG"
, struct="HAR"
, and struct="UN"
), and whether the component was fixed or estimated. Finally, either the estimate of $\rho$ (for struct="CS"
, struct="AR"
, struct="HAR"
, or struct="HCS"
) or the entire estimated correlation matrix (for struct="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. If there is a second ~ inner | outer
formula term, the same information as described above will be provided, but now for the \gamma² and $\phi$ components.
"rma.uni"
and "rma.glmm"
, the I² statistic. For a random-effects model, I² 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, I² estimates how much of the unaccounted variability (which is composed of residual heterogeneity plus sampling variability) can be attributed to residual heterogeneity. See Note for how I² is computed.
"rma.uni"
and "rma.glmm"
, the H² statistic. 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. See Note for how H² is computed.
"rma.uni"
, the R² statistic, which estimates the amount of heterogeneity accounted for by the moderators included in the model and can be regarded as a pseudo R² statistic (Raudenbush, 2009). Only provided when fitting a mixed-effects models (i.e., for models including moderators). This is suppressed (and set to NULL
) for models without moderators, fixed-effects models, or if the model does not contain an intercept. NA
if the amount of heterogeneity is equal to zero to begin with. See Note for how R² is computed.
"rma.glmm"
, the amount of study level variability (only when using a model that models study level differences as a random effect).
"rma.glmm"
, the results from a Wald-type test and a likelihood ratio test are provided (see rma.glmm
for more details).
"rma.mh"
).
Lopez-Lopez, J. A., Marin-Martinez, F., Sanchez-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, 30--48.
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/.
rma.uni
, rma.mh
, rma.peto
, rma.glmm
, rma.mv