[R-sig-ME] Over-dispersion and lmer

[ONKELINX, Thierry]

Dear Ricardo,

You can add an observation-level random effect. That will capture the overdispersion.

yourData$ID <- seq_len(nrow(yourData))
m1 <- glmer(richness~leafs*galls+(1|Plant) + (1|ID),family=poisson, data = yourData)

MCMCglmm uses the same trick to model overdispersion.

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
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-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Ricardo Solar
Verzonden: maandag 19 december 2011 14:20
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] Over-dispersion and lmer

Hi folks;

I'm trying to adjust a model with lmer as follows:

m1 <- glmer(richness~leafs*galls+(1|Plant),family=poisson) ...

It's everything OK with the model and results, however I know by standard glm that this data is VERY over-dispersed. How do you do to solve this issue? Is there any package or analytical solution to that?

Thanks in advance!

RSolar
--
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Ricardo Ribeiro de Castro Solar (Curriculo Lattes<http://lattes.cnpq.br/9924177207371692>
)
MSc. em Entomologia - Doutorando - PPG Entomologia Laboratório de Ecologia de Comunidades/Formigas <http://www.labecol.ufv.br>
(31) 3899-4018 - Contato Skype: rrsolar
Universidade Federal de Viçosa - MG
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