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

Mon Feb 1 04:53:33 CET 2016

Ah, that's quite nice! Thanks so much to everyone for sharing all of this
information.

M

On Sun, Jan 31, 2016 at 9:02 PM, Alex Fine <abfine at gmail.com> wrote:

> I always liked this way of visualizing mixed logit models:
> https://hlplab.wordpress.com/2009/01/19/plotting-effects-for-glmer-familybimomial-models/
>
> On Sun, Jan 31, 2016 at 7:35 PM, M West <m.westinbrook at gmail.com> wrote:
>
>> aha, you are right - sorry, I received a weird error message earlier
>> saying
>> it wasn't available for glmer....that doesn't appear now. thanks.
>>
>> On Sun, Jan 31, 2016 at 5:59 PM, Fox, John <jfox at mcmaster.ca> wrote:
>>
>> > Dear M.,
>> >
>> > The effects package does work with GLMMs fit with glmer() in the lme4
>> > package. See ?Effect. Here's an example adapted from ?glmer:
>> >
>> >         library(effects)
>> >         library(lme4)
>> >         library("HSAUR2")
>> >         gm2 <- glmer(outcome~treatment*visit+(1|patientID),
>> >                  data=toenail, family=binomial, nAGQ=20)
>> >         Effect(c("treatment", "visit"), gm2)
>> >
>> > producing
>> >
>> > treatment*visit effect
>> >               visit
>> > treatment              1         2          3          4           5
>> >      6            7
>> >   itraconazole 0.2236820 0.1155113 0.05588527 0.02612852 0.012014461
>> > 0.005481597 0.0024920184
>> >   terbinafine  0.2104865 0.0871212 0.03303451 0.01208159 0.004358651
>> > 0.001564643 0.0005606578
>> >
>> > I hope this helps,
>> >  John
>> >
>> > -----------------------------
>> > John Fox, Professor
>> > McMaster University
>> > Hamilton, Ontario
>> > Canada L8S 4M4
>> > Web: socserv.mcmaster.ca/jfox
>> >
>> >
>> >
>> >
>> > > -----Original Message-----
>> > > From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-
>> > > project.org] On Behalf Of M West
>> > > Sent: January 31, 2016 11:29 PM
>> > > To: Phillip Alday <Phillip.Alday at unisa.edu.au>
>> > > Cc: r-sig-mixed-models at r-project.org
>> > > Subject: Re: [R-sig-ME] Plotting best fit lines binomial GLMM
>> > >
>> > > 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-o
>> > > > f-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]]
>> > > > >
>> > > > > _______________________________________________
>> > > > > R-sig-mixed-models at r-project.org mailing list
>> > > > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> > > > >
>> > > >
>> > >
>> > >       [[alternative HTML version deleted]]
>> > >
>> > > _______________________________________________
>> > > R-sig-mixed-models at r-project.org mailing list
>> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> >
>>
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>>
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>
>
>
> --
> Alex Fine
> Ph. (336) 302-3251
> web:  http://internal.psychology.illinois.edu/~abfine/
> <http://internal.psychology.illinois.edu/~abfine/AlexFineHome.html>
>

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