Hi Michael, I think you're right, it would be a good idea for me to get a better grip on mixed effects modeling before I charge ahead with metafor. Thank you very much for all of your help with this! Much appreciated!
Best, Megan On Thu, Jul 17, 2014 at 3:50 AM, Michael Dewey <i...@aghmed.fsnet.co.uk> wrote: > At 17:49 16/07/2014, Megan Bartlett wrote: > >> 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? >> > > No. > > Since the climate variable is per study (I assume) you are assuming that > it has the same effect on each species. If that is not true you need to add > species as another moderator and then add the interaction between climate > and species. > > The random parameter is saying that each site has its own intercept but > you are only estimating its variance and each species also has its own > intercept drawn from another distribution whose variance is being estimated. > > I think you probably need to get local statistical help now from someone > who understands the science of what you are doing and the statistics of > mixed effects models. I am a bit concerned that without that knowledge we > on the list may end up giving you misleading advice, > > > > 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]] >> > > 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.
