[R-meta] Meta-analysis of R^2 Values
Hanel, Paul H P
p@h@ne| @end|ng |rom e@@ex@@c@uk
Wed Jun 7 12:11:32 CEST 2023
Thank you for making it possible to meta-analyse R-square values. Based on your documentation I take you are referring to the standard R-square values, not the adjusted one?
By entering the number of predictors mi, is your function computing the adjusted R-square values? If so, how would you run a meta-analysis with the adjusted R-squares?
PS: I am happy to share the dataset with R-square values I am currently working on as soon as its finalised.
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> On Behalf Of Viechtbauer, Wolfgang (NP) via R-sig-meta-analysis
Sent: 01 June 2023 13:51
To: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-project.org>
Cc: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Subject: [R-meta] Meta-analysis of R^2 Values
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On a number of occasions, the question has been raised on this mailing list whether it is possible to meta-analyze R^2 values (I have also received this question a number of times via email). See, for example:
In these discussions, valid concerns about this have been raised. For example, R^2 values are 'directionless' (in contrast to the more commonly used outcome measures used for meta-analyses, where positive and negative values can cancel each other out). The question is also how to compute the sampling variance of R^2 values and whether some kind of transformation may be needed (to normalize the sampling distribution).
I share (and raised some of) these concerns but I would also say that it is not inherently wrong to meta-analyze R^2 values. Therefore, after a bit of further reading, thinking, and running some simulations, I have now implemented measures "R2" and "ZR2" in escalc(). The former is for raw R^2 values, although it should be better to use the latter as it uses a variance-stabilizing transformation of R^2 that also has normalizing properties (similar to the well-known r-to-z transformation for raw correlation coefficients). You can find the documentation about this here:
(if you search for 'R-squared', you will find the right place in this ever growing help page).
Some of the caveats / limitations are also mentioned there (e.g., the equations assume that we are in a multivariate normal setting and that the true R^2 values are non-zero).
If you want to try this out, first install the 'devel' version of metafor:
and then this will work:
dat <- dat.aloe2013
dat <- escalc(measure="R2", r2i=R2, mi=preds, ni=n, data=dat, slab=study) res <- rma(yi, vi, data=dat) res forest(res, header=TRUE, xlim=c(-0.6,1.4), alim=c(0,1), refline=coef(res), efac=2) title(expression(bold("Using Raw " * R^2 * " Values")))
dat <- escalc(measure="ZR2", r2i=R2, mi=preds, ni=n, data=dat, slab=study) res <- rma(yi, vi, data=dat) res pred <- predict(res, transf=transf.ztor2) pred forest(res, header=TRUE, xlim=c(-0.6,1.4), alim=c(0,1), transf=transf.ztor2, refline=pred$pred, efac=2) title(expression(bold("Using z-transformed " * R^2 * " Values (back-transformed)")))
I cannot say whether a meta-analysis of the R^2 values for this particular dataset is sensible. Just using it for illustration purposes.
If somebody has a dataset with R^2 values where they have a legitimate reason for such a meta-analysis, I would love to hear about it. Any feedback in general is of course welcome.
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