Hi:

No direct answers, but some questions...

On Tue, Aug 24, 2010 at 2:01 PM, Richard Feldman <
richard.feldman@mail.mcgill.ca> wrote:

> Hello,
>
> I am at my wit's end with regards to specifying my model, perhaps because I
> am confused about nested vs. crossed grouping factors.
>
> My dataset has 16 sites and within each site I applied 3 treatments (A, B,
> C). The sites differ based on elevation. I originally thought I had a
> hierarchical (nested) model and specified the full model as such:
>
> How did you assign treatments within site? Were they assigned to divisions
of a site (e.g., subplots) or were they assigned to the entire site at
different times, or ???  This matters in the analysis...a lot.



> model.n <- glmer(Y ~ Treatment*Elevation + (Treatment|Site), data=Data)
>
> The data also seemed analogous to a longitudinal model where instead of
> subject I have site and instead of time/days I have treatment. I am not
> totally clear on why this analogy breaks down.
>

Longitudinal models involve a time element, usually within subject/primary
unit, and constitute repeated measurements on that unit over time with the
same treatment conditions and possibly time-varying covariates. How would
such a scenario correspond to your design?


>
> After extensive reading, it seems that because each site receives the same
> three treatments, my model is crossed and not nested. Hence the
> specification should be:
>

It's not obvious at this point whether you have crossed or nested effects.
It's entirely possible that site could be a blocking factor. Go back to the
initial question.


> model.c <- glmer(Y ~ Treatment*Elevation + (1|Site) + (1|Treatment),
> data=Data)
>
> I have three questions:
>
> 1. Is model.c indeed the correct specification given my data?
>
> 2. Given model.c, does the treatment by elevation interaction capture this
> cross-scale effect, even though the former is a level-1 predictor (varies
> within site) and the latter a level-2 predictor (varies among sites)?
>
> 3. The output from model.c gives zero variance for the random effect of
> treatment. I assume this is because there are only three levels. Hence,
> treatment can only be a fixed variable. I have no problem with that. What I
> am confused about is how I can discover how much the treatment-response
> relationship varies among sites. I originally thought that (Treatment|Site)
> made sense because the treatment-response slope could vary based on site.
>

I don't think we have enough information yet to make a determination on any
of your questions.

Hope this helps somewhat,
Dennis

>
> I appreciate all your help in getting me out of this mental quagmire. Thank
> you!
>
> --
> Richard Feldman, PhD Candidate
> Dept. of Biological Sciences, McGill University
> W3/5 Stewart Biology Building
> 1205 Docteur Penfield
> Montreal, QC H3A 1B1
> 514-212-3466
> richard.feldman@mail.mcgill.ca
>
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