Plotting best fit lines binomial GLMM

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.

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