using Poisson glmer for non-integer data
-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 [cc'ing to r-sig-mixed-models] I don't think it's crazy to use a Poisson distribution with non-integer response values in some cases, but you're correct that non-integer response values don't work in lme4 at present. In principle we could dig down and fix the problem (feel free to post an issue at https://github.com/lme4/lme4/issues, with a simple reproducible example ...) but I have to say it's not very high on our list, because these are (usually? often?) cases where the original model is somewhat suspect anyway. I can suggest the following workarounds: * try another package such as glmmML or glmmPQL * use an offset that characterizes the total lifespan; if you use offset(log(lifespan)) that will effectively model success per unit lifespan, and if you include log(lifespan) as a predictor (with the standard log link) that will effectively model success as proportional to (lifespan)^b, where b is a parameter to be estimated. Other discussion of Poisson with non-integer values http://www.r-bloggers.com/poisson-regression-on-non-integers/ http://stats.stackexchange.com/questions/38530/how-does-a-poisson-distribution-work-when-modeling-continuous-data-and-does-it-r/38588#38588 http://stats.stackexchange.com/questions/70054/how-is-it-possible-that-poisson-glm-accepts-non-integer-numbers sincerely Ben Bolker
On 14-08-10 09:08 PM, Christina Painting wrote:
Dear Prof Bolker, I'm a behavioural ecologist at the University of Auckland in New Zealand, and I currently have a masters student who is tackling some lifetime mating success data for the NZ giraffe weevil. We were hoping you might be able to offer us some advice on an issue we are having using the /lme4/ R package. Our response variable is the average mating success of a giraffe weevil for its lifetime (total success/lifespan) and we are looking at this in relation to body size and time of year. Using your 2008 TREE paper on using GLMMs we figured out that the best method to use was a model with Poisson distribution with Laplace approximation because av. mating success is non-normally distrubited, can't be fixed with standard transformations and has a mean <5. However, because the data are not integers we have run into problems, with the models returning warnings about the data being non-integer, and then we can't get log-lik and AIC values. Reading online on various forums that you have been part of suggests we aren't the only ones having this problem, and I wondered if you had any solutions to this problem, or could suggest another method to use that would be robust to our average measure of mating success? We would greatly appreciate any advice you can offer, and thank you in advance Kind regards, Chrissie Painting *Dr Chrissie Painting* Post Doctoral Researcher School of Biological Sciences University of Auckland cpai015 at aucklanduni.ac.nz <mailto:cpai015 at aucklanduni.ac.nz> https://sites.google.com/site/paintingchristina/ Mobile: +64 27 306 1610
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