[R] Correlating multiple effect sizes within a study to study-level predictors: metafor package

[Michael Dewey]

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.viechtbauer at 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-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> > > On Behalf Of Michael Dewey
> > > Sent: Monday, July 14, 2014 14:42
> > > To: Megan Bartlett; r-help at 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
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