Beta-binomial distributions with lmer?
Dear Christine, We had recently a vivid discussion on whether it is appropriate to model percentages by a (quasi)binomial model. We were modelling the precentage of leaves that is missing from trees. The mixed model with the binomial family had random effects with extremly small variances. My colleague argued that this percentage did not come from a bernouilli experiment. And hence the binomial family was not appropriate. He suggested to put the percentage on a 0 to 100 scale and apply a log(x+1) transformation. This resulted in a linear mixed model with random effects that had reasonable variances. This convinced me that the binomial family only makes sense with binary data. HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 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 Ben Bolker Verzonden: woensdag 10 juni 2009 15:59 Aan: Christine Griffiths CC: r-sig-mixed-models at r-project.org Onderwerp: Re: [R-sig-ME] Beta-binomial distributions with lmer? That's a good question, answers will differ. Since "all models are wrong" anyway, provided that a mean-variance relationship of V = phi*N*p*(1-p) seems plausible, I would say you should go for it. You're near the cutting edge anyway ... (I don't have a copy, but you might see whether Zuur et al's book has anything to say on the subject -- they're very pragmatic ecologists, and I think they use GEE/quasi models quite a lot ...) Ben Bolker
Christine Griffiths wrote:
Thanks. I was hoping for a miracle that this had been developed within
the last couple of months. I am on the stats learning curve and am not quite sure how flexible to
be with regards to distributions. Is quasibinomial acceptable, despite having data with a lot of 0s and a lot of 100s? Many thanks in advance, Christine --On 10 June 2009 09:18 -0400 Ben Bolker <bolker at ufl.edu> wrote:
No. You can use a quasi-binomial model, although the support is a little bit spotty (and beware that quasi- models may falsely report inflation of the random effects). Ben Bolker Christine Griffiths wrote:
Hi R users, Just a query as to whether lme4 can handle beta-binomial distributions as I read that this was not available. If not, any suggestions on how to handle such a distribution to plot
the following model: y<-cbind(Biotic,Abiotic) m1<-lmer(y~Treatment+Month.rain+(1|Month)+(1|Block/EnclosureID/Quadr at)) y referring to percentage cover of biotic matter. Cheers, Christine
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-- Ben Bolker Associate professor, Biology Dep't, Univ. of Florida bolker at ufl.edu / www.zoology.ufl.edu/bolker GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document.