[R-sig-eco] Mixed effects models with ordinal response variable

[ONKELINX, Thierry]

Dear Omer,

How much data do you have? And how strong is the effect of the variables? Do you get similar parameter estimates from clmm and MCMCglmm?

It's not uncommon that when you have plenty of data non-relevant (small) effects become significant.

Best regards,

Thierry

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: r-sig-ecology-bounces at r-project.org [mailto:r-sig-ecology-bounces at r-project.org] Namens Omer Nevo
Verzonden: maandag 23 april 2012 11:54
Aan: r-sig-ecology at r-project.org
Onderwerp: [R-sig-eco] Mixed effects models with ordinal response variable

Hello everybody,

I have a dataset with an ordinal response of 5 levels which wish to model on 4 potential predictors. There's a lot of repeated measurements (of individual howler monkeys) so a mixed model is required.

I tried pretending the response was a continuous variable but received an extremely structured residuals pattern indicating the model was flawed.
I then tried clmm2 (package "ordinal") and MCMCglmm (package "MCMCglmm") which should work with ordinal variables. Both seem to be "over optimistic" and find significant results where I don't expect them to be (essentially in all predictors). The latter's under-conservatism with my data is also pronounced by the fact that it found significant or close to significant results in a completely random variable I generated and included in the model.

Does anyone have an advise or some good experience with mixed models of an ordinal response?

Best,
Omer

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