Hi Michael, Thank you! Just to clarify, in my question, I was thinking that in this regression each study should be treated as one point, instead of each species, so that each effect size x value has a unique climate y value. Is that what the random= list(~1|Species, ~1|Site) argument is doing?
Thanks, Megan On Wed, Jul 16, 2014 at 1:53 AM, Michael Dewey <i...@aghmed.fsnet.co.uk> wrote: > At 23:19 14/07/2014, Megan Bartlett wrote: > >> 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? >> > > Yes, just put it in as a moderator. > > I am not sure I fully understand the rest of your question but the answer > may be that the weights are a property of the individual effect sizes > > 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-bounces@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]] >> > > Michael Dewey > i...@aghmed.fsnet.co.uk > http://www.aghmed.fsnet.co.uk/home.html > > [[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.
