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

	[[alternative HTML version deleted]]

