Thanks very much, Wolfgang and Michael! I feel like I understand rma much more clearly.
But just to make sure, is there any way to do this kind of analysis for a continuous predictor variable? For each site level, I have a value for a climate variable, and it would be great to see whether the average effect size for each site is correlated with that climate variable. But I'm not sure what variance would produce the appropriate weighting for each site-level average- would it be the variance in effect sizes across species within each site? Or does this analysis not really make any sense for effect sizes? Thanks again! Best, Megan On Mon, Jul 14, 2014 at 6:06 AM, Viechtbauer Wolfgang (STAT) < wolfgang.viechtba...@maastrichtuniversity.nl> wrote: > Somehow that initial post slipped under the radar for me ... > > Yes, I would give the same suggestion as Michael. Besides random effects > for 'site', I would also suggest to add random effects for each estimates > (as in a regular random-effects model). So, if you have an 'id' variable > that is unique to each observed d-value, you would use: > > random = list(~ 1 | site, ~ 1 | id) > > with the rma.mv() function. This is in essence the model given by > equation (6) in: > > Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and > advances in biological meta-analysis. Evolutionary Ecology, 26(5), > 1253-1274. > > (at the time of publication, this model could not be fitted with metafor, > but it can now). Same model is described with a bit more detail in: > > Konstantopoulos, S. (2011). Fixed effects and variance components > estimation in three-level meta-analysis. Research Synthesis Methods, 2(1), > 61-76. > > 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 Michael Dewey > > Sent: Monday, July 14, 2014 14:42 > > To: Megan Bartlett; r-help@r-project.org > > Subject: Re: [R] Correlating multiple effect sizes within a study to > > study-level predictors: metafor package > > > > At 23:18 11/07/2014, Megan Bartlett wrote: > > >Hi everyone, > > > > > >Since metafor doesn't have its own list, I hope this is the correct > > place > > >for this posting- my apologies if there is a more appropriate list. > > > > metafor questions welcome here, Megan > > > > Wolfgang seems to be off-list so while we wait for the definitive > > answer here are some hints. > > > > > > >I'm conducting a meta-analysis where I would like to determine the > > >correlation between plasticity in leaf traits and climate. I'm > > calculating > > >effect sizes as Hedge's d. My data is structured so that each study > > >collected data from one forest site, so there is one set of climate > > >variable values for that study, and there are one or more species in > > each > > >study, so all the species in a study have the same values for the > > climate > > >variables. I'm not sure how to account for this structure in modeling > > the > > >relationship between plasticity and climate. > > > > I think you need rma.mv for your situation and you need to specify a > > random effect for site. > > > > Try going > > ?rma.mv > > and looking for the section entitled Specifying random effects > > You will need to set up your dataframe with one row per species and > > an indicator variable for site and then use > > random = ~ 1 | site > > > > Not tested obviously and Wolfgang may have other suggestions > > > > >My first thought was to calculate mean effect size and variance across > > >species for every study with multiple species and correlate that with > > >the climate variable values for those study with the rma() function, but > > >trying to do that returns an error message: > > > > > >rma(yi = EffectSize, vi = Var, data = sitestable, mod = Precip) > > >returns: Error in wi * (yi - X %*% b)^2 : non-conformable arrays > > > > > >This leaves me with two questions: 1) Am I even accounting for the data > > >structure correctly with this approach, and 2) am I fundamentally > > >misunderstanding how to use metafor to do so? > > > > > >Thanks very much for your help! > > > > > >Best, > > > > > >Megan > [[alternative HTML version deleted]] ______________________________________________ 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.
