Gavin,
Thanks for the comment. I have since come to the understanding that my data
does indeed have a binomial distribution. It is proportional data. It was a
little weird in that I had to create an aggregate variable from the
successes (#of EPT per site) and failures (number of invertebrates not EPT)
using the following code.
E<-cbind(eptCNT, n-eptCNT)

my current model is of the form:
modelB<-glmer(E ~ wsh*rip + (1|stream) + (1|stream:rip), data=ept,
family=binomial(link="logit"))

Thanks for the heads up, anyway, I appreciate it. I've been dealing with
overdispersion in another thread, which has become quite the goose chase!

Colin


On Mon, Mar 7, 2011 at 1:45 AM, Gavin Simpson <gavin.simpson@ucl.ac.uk>wrote:

> On Tue, 2011-02-01 at 14:14 -0800, Colin Wahl wrote:
> > Dear r-sig-mixed-models List,
> >
> > I've been digging through the r-help archive and various books in search
> of
> > an answer to my query. I have been thus far unsuccessful. I am not new to
> R,
> > but have primarily focused on multivariate non parametric analyses in the
> > past; my experience with lmer has been confined to the last few weeks.
> The
> > model formulas I have written work and I have some idea of which are
> > accurate/best based on AIC scores, but I would very much appreciate an
> > informed opinion on my syntax.
> >
> > I am performing an ecological study of stream health using %EPT (An
> > aggregate relative abundance variable for invertebrates) as my response
> > variable. I am interested in how invertebrate counts vary between
> different
> > watershed types (forested, cultivated, developed, grassland) and reach
> types
> > (forested, non forested). There are a total of 12 streams, 12 watersheds
> and
> > 24 reaches. The wshed factor is unbalanced.
>
> Hi Colin,
>
> I have very little to add to the discussion thus far (from which I have
> learned a lot by the way!), except to question the use of family =
> gaussian for this response? %EPT will be bounded at zero (there will be
> samples for locations that are so polluted as to not have any of these
> critters living there), and potentially bounded at 100% (theoretically
> at least, although I find it hard to see how one would get 100% of this
> group of inverts in a kick sample). The errors may depart from the
> assumptions of the LMM.
>
> If you know the total counts then perhaps a binomial GLMM would be
> appropriate. If not, beta regression via the betareg package might be an
> alternative - the sandwich package can be used to  fix up the covariance
> matrix to account for the clustering in the data.
>
> Just my tuppence,
>
> G
>
> > Data structure:
> > > str(ept)
> > 'data.frame':    72 obs. of  4 variables:
> >  $ wshed  : Factor w/ 4 levels "c","d","f","g": 1 1 1 1 1 1 1 1 1 1 ...
> >  $ stream : Factor w/ 12 levels "BA","D1","D2",..: 4 4 4 6 6 6 10 10 10 4
> > ...
> >  $ riparia: Factor w/ 2 levels "F","N": 1 1 1 1 1 1 1 1 1 2 ...
> >  $ ept    : num  0.318 0.156 0.194 0.146 0.158 ...
> >
> > stream is the only random variable and it is nested (not implicitly) in
> > wshed. My standard statistical model is as follows:
> > ept(ijkl) =  + wshedi  + stream(i)j + ripariak + wshed*ripariaik +
> > stream*riparia(i)jk + (ijk)l
> >
> > The best option I have come up with thus far is:
> > >model<-lmer(ept ~ wshed + riparia + wshed:riparia + (1| wshed/stream) +
> (1|
> > stream:riparia), data=ept, family="gaussian")
> > or alternatively:
> > >model<-lmer(ept ~ wshed*riparia + (1| wshed/stream) + (1|
> stream:riparia),
> > data=ept, family="gaussian")
> >
> > Question: Am I specifying the random terms and nesting structure
> correctly?
> >
> > Secondarily: I have read a few times that "wshed/stream" is
> interchangeable
> > with  "wshed:stream" which is a meaningless interaction. Also I've seen
> that
> > random effects are specified as (a|b) where a is a covariate and b is a
> > grouping factor. Does having 1 as a covariate simply specifying an
> intercept
> > of 1? What is the purpose of placing a factor in the place of 1 as a
> > covariate? OR is there a nice complete summary tutorial that I've missed.
> >
> >
> > I'm looking forward to hearing any comments.
> >
> > Thank you,
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
> --
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>  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
>  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
>  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
>  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
>  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
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>
>


-- 
Colin Wahl
Department of Biology
Western Washington University
Bellingham WA, 98225
ph: 360-391-9881

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