Tonio Pieterek <t.pieterek <at> googlemail.com> writes: >
[snip] This is really more appropriate for r-sig-mixed-mod...@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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
