Contents
How is the RMA function used in METAFOR?
Function to fit the meta-analytic fixed- and random/mixed-effects models with or without moderators via linear (mixed-effects) models. See the documentation of the metafor-package for more details on these models.
How are random effects fitted to rma.uni ( ) function?
A random-effects model can be fitted to these data using the rma.uni () function with: The default for the method argument is to use REML estimation for the amount of heterogeneity and a random-effects model is then automatically fitted. When using the rma.mv () function, random effects must be explicitly added to the model via the random argument.
How is the meta-analytic random-effects model conceptualized?
In essence, the meta-analytic random-effects model can be conceptualized as a multilevel model with the true effects at level 2 and the observed effects at level 1. Using typical multilevel model terminology, the random = ~ 1 | trial argument adds random intercepts at level 2 to the model.
When to use the rma.mv ( ) function?
For multilevel, multivariate, and network meta-analyses, the package also provides the rma.mv () function, which allows for correlated sampling errors and/or true effects.
Is the linear regression model the same as the mixed effect model?
Models of this sort can be fitted with the function rma () from the metafor package. Consequently, if one were to set vi = 0 v i = 0 for all outcomes, then the standard linear regression model and the mixed-effects meta-regression model are actually identical (with σ2 σ 2 denoting the same parameter as τ 2 τ 2 ).
What is the pseudo R-squared value of RMA?
The (pseudo) R-squared value reported by rma () ( 89.83%) actually corresponds to the adjusted R-squared value provided by lm () ( 0.8983 ). The I 2 I 2 and H 2 H 2 statistics typically reported by the rma () function are missing, since these statistics cannot be computed when the dataset includes outcomes with non-positive sampling variances.
How to do a meta-analysis with R?
Random-Effects Model (k = 13; tau^2 estimator: REML) • tau^2 (estimated amount of total heterogeneity): 0.3132 tau (square root of estimated tau^2 value): 0.5597 I^2 (total heterogeneity / total variability): 92.22% H^2 (total variability / sampling variability): 12.86 Test for Heterogeneity: predict(res, transf=<>, digits=2)
When did the METAFOR package become available in R?
•several meta-analysis packages •all lacked meta-regression capabilities •wrote my own function (mima) in 2006 •turned into full package (metafor) in 2009 •Viechtbauer, W. (2010). Conducting meta- analyses in R with the metafor package.
How are multiple moderators specified in rma.uni?
A single moderator can be given as a vector of length \\ (k\\) specifying the values of the moderator. Multiple moderators are specified by giving a matrix with \\ (k\\) rows and as many columns as there are moderator variables.
What’s the difference between RMA and rma.uni ( )?
Again, using rma () is the same as invoking the rma.uni () function, but the former is quicker to type. The same model can be fitted to these data using the rma.mv () function with: The results are exactly the same. In both functions, predictors/covariates/moderators can be included in the model via the mods argument. For example: