[R-sig-ME] Plotting best fit lines binomial GLMM

[M West]

Main questions:
(1) How to extract coefficients from GLMM to plot best fit lines to data?
(2) Are there other options for dealing with these sorts of data besides
mixed effects models (or RM ANOVA)?


Specifics: I have a short time series data across 12 sites over 8 years.
I'd like an omnibus plot that summarizes the main pattern interest in these
data.

The dependent variable is frequency females (data are # smokers out of the
total population). The independent variable is also a frequency (# infected
out of the total population).

Plotting each year separately it's easy to see the positive correlation
between smokers and infection. However, given the variation among years,
plotting all the
data on a single plot obscures the overall pattern....I need to fit
regression lines to
each year.

I know how to do this with lme....but I can't quite find how to do this
with GLMM and I've analyzed the data with a GLMM with a binomial
distribution (following Crawley)
[While the data are binomial, they are not binary (i.e., not 0 and 1)so a
logistic curve doesn't work].


I found this thread on inspecting the residuals but I haven't been able to
find anything on plotting a best fit line for these type of data.

http://stats.stackexchange.com/questions/70783/how-to-assess-the-fit-of-a-binomial-glmm-fitted-with-lme4-1-0


I would *much prefer* to use something other than mixed effects models (I
think the results are not straightforward to interpret and every book or
blog recommends a different approach) for this analysis so if there are
other suggestions they are also welcome!

Thanks,
M.

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