At the moment, there is no possibility of specifying the weights with the rma() function. While the main model fitting part could be easily adapted to incorporate user-specified weights, the problem comes in with all the additional statistics that can be computed based on a fitted model. How should the predict() function now work? What would be the definition of I^2 now? How would one generalize the influence and outlier statistics to that case? Just to give some examples.
Of course, I could leave out such things when the user has specified the weights, but then things also get confusing for the user. For example, it is already less than ideal that you can only use the trim and fill method with models that do not incorporate moderators. Nobody has (as of yet) generalized the trim and fill method to that case. But when there are too many "special cases", the package becomes unusable. I did consider user-specified weights at one point, but it opened up so many cans of worms that I preferred to quickly put the lid pack on those cans. That item is written down in my to-do list, but to be honest, it is somewhere at the very end of that list. If you are hesitant to combine the results from those two types of studies, what about simply using a moderator to distinguish the two groups? Best, Wolfgang -- Wolfgang Viechtbauer, Ph.D., Statistician Department of Psychiatry and Psychology School for Mental Health and Neuroscience Faculty of Health, Medicine, and Life Sciences Maastricht University, P.O. Box 616 (VIJV1) 6200 MD Maastricht, The Netherlands +31 (43) 388-4170 | http://www.wvbauer.com > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf Of Anke Stein > Sent: Thursday, May 24, 2012 15:59 > To: r-help@r-project.org > Subject: [R] package metafor: specify weights? > > Dear R-experts, > Dear Wolfgang, > > Weighted model fitting in metafor uses the inverse of the study specific > variances as weights. > I am wondering if it is possible to specify different weights. > > In my meta-analysis, there are two types of studies with (intrinsic) > differences in their range of sample sizes (which are used to calculate > the variances of Fisher's z). > I would like to try normalizing the sample sizes within each set of the > two study types and use these normalized sample sizes as weights. > Would that be possible with rma()? So far, I only found the option > "weighted = TRUE/FALSE", but no possibility to specify which weights > should be used. > > Many thanks in advance, > Anke > > > > > > > -- > __________________________________________________________ > Anke Stein (Dipl.-Biol.) > > Biodiversity, Macroecology & Conservation Biogeography Head Prof. Dr. > Holger Kreft Georg-August University of Göttingen Büsgenweg 2 | 37077 > Göttingen | Germany > > phone +49(0)551-39-13761 > fax +49(0)551-39-3618 > ast...@uni-goettingen.de > http://www.uni-goettingen.de/biodiversity > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
