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

[Michael Dewey]

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 <info at 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.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
> > info at aghmed.fsnet.co.uk
> > http://www.aghmed.fsnet.co.uk/home.html
> >
> >
>
>         [[alternative HTML version deleted]]

Michael Dewey
info at aghmed.fsnet.co.uk
http://www.aghmed.fsnet.co.uk/home.html