[R] Differences between glmmPQL and lmer and AIC calculation
Ben Bolker
bbolker at gmail.com
Fri Jul 12 02:42:47 CEST 2013
Tonio Pieterek <t.pieterek <at> googlemail.com> writes:
>
[snip]
This is really more appropriate for r-sig-mixed-models at r-project.org :
please refer any followup questions there.
> For my Master thesis I collected some behavioral data of fish using
> acoustic telemetry. The aim of the study is to compare two different
> groups of fish (coded as 0 and 1 which should be the dependent
> variable) based on their swimming activity, habitat choice, etc.
> (independent variables).
I don't quite understand this part. You're trying to figure out
whether a particular observation falls into one category or
another (0/1)? Do individual fish (id in the formula below)
always fall into one category or the other?
> Each fish has several observations over time
> (repeated measurements) which I included as random factor in my models
> using library glmmPQL (package MASS). Because I have a binary data
> structure, I am using generalized linear mixed models.
For what it's worth, penalized quasi-likelihood (used by glmmPQL)
is generally considered to be a bit dicey with binary responses
(see e.g. Bolker et al 2008 TREE paper).
> Using library glmmPQL the results reflect my descriptive analyses and
> the results are sound. However, we also want to rank several candidate
> models using AIC. And this is where the problems start. Because
> glmmPQL does not provide AIC values or comparable measures, I also
> tried to calculate the same models using function lmer. Against
> expectations, I got completely different results from these two
> libraries (glmmPQL = highly significant; lmer = far away from being
> significant with p = 0.9xx).
>
> I used the following codes:
>
> cal1=glmmPQL(y ~ activity, random=~1|id, data=data, family=binomial,
> na.action=na.omit)
>
> > WORKS FINE
>
> cal1 = lmer(y ∼ activity + (1 | id ), family = binomial, data=data,
> na.action=na.omit)
>
> > PRODUCED misleading and totally different results compared to glmmPQL
> (e.g. sometimes error message
> occurs: In mer_finalize(ans) : false convergence (8); even
> for very simple models)
Do you possibly have complete separation, i.e. some individuals
with all-zero responses?
Have you tried the development version of lme4?
>
> A glmmML did not work since we got the following failure message, for
> which we were not able to find out the reason and therefore could not
> go on with this model:
>
> “[glmmml] fail = 1
[snip]
> The questions are:
>
> 1) Why did glmmPQL and lmer produce completely different results and
> how can I solve this problem? Following Zuur et al. 2009* the models
> should provide very similar results, but they didn`t.
I strongly suspect that there's something wrong with your setup.
In particular, if the response variable cal1 (0/1) only varies at
the level of individuals (id), and not within id, then you should
probably just calculate the mean activity per individual id and
run a simple logistic regression. My guess would be that glmmPQL
may have papered over some cracks for you ...
> 2) Can I calculate AIC values (or something comparable)
> using library glmmPQL?
No, not without a great deal of difficulty.
>
> 3) Is there any other option (library) to analyze my data including an AIC?
You can use JAGS/WinBUGS with data cloning (the dclone package), or
glmmADMB.
>
> If something remained unclear or if you have any question about
> details, please let me know.
>
> I would really appreciate any kind of help referring to my problem(s).
>
>
> *Alain F. Zuur, Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev,
> Graham M. Smith. (2009). Mixed Effects Models and Extensions in
> Ecology with R. Springer Science+Business Media, New York, USA.
>
> ISSN 1431-8776
> ISBN 978-0-387-87457-9
> DOI 10.1007/978-0-387-87458-6
I suspect
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