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

[M West]

Thanks for this suggestions Philip  - it looks like the effects package
doesn't work
for GLMMs - it works with glms.....

On Sun, Jan 31, 2016 at 1:05 AM, Phillip Alday <Phillip.Alday at unisa.edu.au>
wrote:

> 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).
>
> Best,
> Phillip
>
> 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.
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>

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