Hello Stephen,
As far as I know, the meta package will not allow you to include moderator
variables in the model. However, I have written a script for R/S-Plus that will
allow you to fit such models (essentially, these are mixed-effects models with a
random intercept). You can find the script here:
http://www.wvbauer.com/downloads.html
Specifically, if you scroll down a bit, you will find the "mima"
function with a tutorial that explains how it can be used. I hope you find this
useful.
Best wishes,
--
Wolfgang Viechtbauer
Department of Methodology and Statistics
University of Maastricht, The Netherlands
http://www.wvbauer.com/
--
Wolfgang Viechtbauer
?Department of Methodology and Statistics
?University of Maastricht, The Netherlands
?http://www.wvbauer.com/
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-
> bounces at stat.math.ethz.ch] On Behalf Of Stephen
> Sent: Sunday, March 12, 2006 11:55 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] meta / lme
>
> Hi
>
>
>
> I'm conducing a meta-analysis using the meta package.
>
>
>
> Here's a bit of code that works fine -
>
> tmp <- metacont(samplesize.2, pctdropout.2, sddropout.2,
>
> samplesize.1, pctdropout.1, sddropout.1,
>
> data=Dataset, sm="WMD")
>
>
>
> I would now like to control for a couple of variables (continuous and
> categorical) that aren't in the equation.
>
>
>
> Is meta inappropriate for these purposes? If so, based on the above
> code, how would I add variables to the equation?
>
>
>
> Perhaps I should use lme weighting on sample size?
>
>
>
> Thoughts appreciated
>
>
>
> Thanks S.
>
>
>
> PS
>
> > version
>
> _
>
> platform i386-pc-mingw32
>
> arch i386
>
> os mingw32
>
> system i386, mingw32
>
> status
>
> major 2
>
> minor 2.1
>
> year 2005
>
> month 12
>
> day 20
>
> svn rev 36812
>
> language R