[R] meta-regression, MiMa function, and R-squared
Viechtbauer Wolfgang (STAT)
Wolfgang.Viechtbauer at STAT.unimaas.nl
Mon Mar 12 12:23:51 CET 2007
Dear All,
I am actually in the process of turning the mima function (with additional functions for predict, resid, and so on) into a full package.
Making the syntax of the function more like that for lm would indeed be useful. However, for that I would have to familiarize myself more with the internals of R to understand how exactly I can make use of the formula syntax.
As for calculating (something like) R^2, there are essentially two approaches I may suggest. I assume you have a vector of effect size estimates "y", the corresponding vector of estimated sampling variances "v", and you have one or more moderator variables "x1" through "xp".
1) Fit the model containing x1 through xp with the mima function and let tau2 denote the estimate of residual heterogeneity from that model. Create a new variable "w <- 1/(v + tau2)". Note that the mima function does nothing else but fit the model with weighted least squares using those weights. So, you could actually use "lm(y ~ x1 + ... + xp, weights=w)" and you should get the exact same parameter estimates. Therefore, "summary(lm(y ~ x1 + ... + xp, weights=w))" will give you R^2. Note that this is the coefficient of determination for transformed data whose meaning may not be entirely intuitive. See:
Willett, J. B., & Singer, J. D. (1988). Another cautionary note about R^2: Its use in weighted least-squares regression analysis. American Statistician, 42(3), 236-238.
for a nice discussion of this.
2) Another approach that is used in the meta-analytic context is this. First estimate the total amount of heterogeneity by using a model without moderators (i.e., a random-effects model). Let that estimate be denoted by "tau2.tot". Next, fit the model with moderators. Let the estimate of residual heterogeneity be denoted by "tau2.res". Then "(tau2.tot - tau2.res)/tau2.tot" is an estimate of the proportion of the total amount of heterogeneity that is accounted for by the moderators included in the model. This is an intuitive measure that has an R^2 flavor to it, but I would not directly call it R^2.
Hope this helps,
--
Wolfgang Viechtbauer
Department of Methodology and Statistics
University of Maastricht, The Netherlands
http://www.wvbauer.com/
-----Original Message-----
From: Christian Gold [mailto:c.gold at magnet.at]
Sent: Monday, March 12, 2007 10:59
To: r-help at stat.math.ethz.ch; wvb at wvbauer.com
Subject: meta-regression, MiMa function, and R-squared
Dear Wolfgang Viechtbauer and list members:
I have discovered your "MiMa" function for fitting meta-analytic
mixed-effects models through an earlier discussion on this list. I think
it is extremely useful and fills an important gap. In particular, since
it is programmed so transparently, it is easy to adapt it for one's own
needs. (For example, I have found it easy to identify and adapt the few
lines I had to change to make the function fit models without intercept
- impossible with one of the commercial packages for meta-analysis). I agree with Emmanuel Charpentier's suggestion that your function would
be even more useful if it was more alike lm or glm (some time in the
future perhaps). For now, one question: How do I calculate the correct
R-squared for models fitted with MiMa?
Thanks
Christian Gold
University of Bergen
www.uib.no/people/cgo022
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