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

Phillip Alday Phillip.Alday at unisa.edu.au
Sun Jan 31 07:05:59 CET 2016

Addressing the plotting issue: look at the effects package. You can
directly plot effects objects (which will yield lattice plots) or you
can convert them to data frames and plot by hand (e.g. if you want more
control and/or ggplot).


On 30/01/16 08:18, M West wrote:
> 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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