Thanks very much Mike. It clears things up immensely now that i'm not thinking
about nesting fixed effects inside random effects, and the model and dfs make
sense.
doug
On 13 Mar 2011, at 21:04, Dunbar, Michael J. wrote:
Hi Doug
Your first model can't be right, as AM/PM is clearly a fixed effect: it has
just two levels and you are only interested in making inferences about those
two levels. So you can't have AMPM on the right of the random part of the
model. Try not to think of nesting fixed effects in random effects. Think
about specifying the structure of the nesting with the random effects, then
the fixed effects are mapped onto the correct level automatically, lme is
clever like that. You can always check the degrees of freedom also.
The second model sounds more sensible although you probably need to tell it
explicitly that AM/PM corresponds to each measurement in your data, something
like:
Mod.lme3 - lme(bird_entropy ~ sound_entropy * landuse + AMPM, random = ~ 1 |
plot/subplot, data=Bird)
This treats subplot as a random effect nested within plot, and each subplot
has two measurements, AM and PM. This should handle the correlation between
AM/PM, and you can plot residuals to see if there is any further residual
correlation. If so, then you can perhaps consider one of the residual
correlation structure models. You could go further and fit a full bivariate
model between AM and PM, have a look at the paper by Doran and Lockwood 2006
for how to do this with lme.
regards
Mike
From: r-sig-ecology-boun...@r-project.org
[r-sig-ecology-boun...@r-project.org] On Behalf Of dougwyu [doug...@gmail.com]
Sent: 13 March 2011 07:29
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco] nested factor for which i would like a parameter
estimate
Hello all,
I am trying to test whether sound diversity predicts bird diversity.
The question i have concerns how to deal with a factor that could be treated
either as a random or fixed factor.
Response: bird_entropy (continuous)
Fixed factors: landtype (factor), sound_entropy (continuous), AM/PM (factor)
Random factor: Plot
The problematic factor is AM/PM.
We have 29 total sampling plots, each of which is measured for
sound_diversity at five subplots, once in the AM (dawn) and again in the PM
(dusk), for a total 10 measurements per plot. (Each plot has only one,
overall measure of bird_diversity).
On the one hand, AMPM is nested within plot, but on the other hand, we would
like to estimate a parameter for AMPM, since we expect different suites of
sound producers (not just birds) at different times of the day. However, it's
reasonable to expect temporal correlation between AM and PM.
The first model seems uncontroversial to me (these are post-model-selection).
Mod.lme1 - lme(bird_entropy ~ sound_entropy * landuse, random = ~ 1 | plot /
AMPM, data=Bird)
But i'm curious to know if this second model is reasonable, and if so, how
would I code the plot variable?
Mod.lme2 - lme(bird_entropy ~ sound_entropy * landuse + AMPM, random = ~ 1 |
plot, data=Bird)
Thanks all,
Doug
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