lme4 - GLMM dispersion parameter?
If Liam is wanting the (approximate) equivalent of a dispersion parameter, then put in an observation level random effect. Create a factor that has one level for each observation, and include this as a random effect (maybe +~1|obs) Genuine poisson variation is uncommon in such contexts -- typically one can expect some clustering. One should accordingly check the fitting of an observation level random effect. John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Mathematics & Its Applications, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. http://www.maths.anu.edu.au/~johnm
On 01/08/2012, at 10:56 PM, Ben Bolker wrote:
Liam Crowther (BIO <L.Crowther at ...> writes:
Dear list users, I'm using lme4 to model the densities of several bee species in response to landscape gradients. For some species I've used just a random intercept and for others I've allowed a random effect of forage quality, this is determined by comparing maximal models with the different random components before refining the fixed effects. The dependent variable is a count at a transect of which there are repeated measures so I'm using a GLMM with Poisson errors, examples of final models below:
hy25<-glmer(Bh~+DATE+bees$X250PCURB+bees$X250PCOSR+bees$X250PCWOO+ (1|TRANSECT), data = bees, family =poisson)
It may work for now but it's ugly and maybe eventually problematic to use bees$ inside the formula: hy25<-glmer(Bh~DATE+X250PCURB+X250PCOSR+X250PCWOO+(1|TRANSECT), data = bees, family =poisson) would be clearer. [snip]
In total there are 338 observations of 42 subjects, is there a general method for extracting a dispersion parameter from models such as these (there are models for 7 spp. at 3 different scales) where there are differing numbers of predictors and random effects?
Don't quite understand the question. Poisson models don't have dispersion parameters. Do you want to extract the random-effects variances and covariances (?VarCorr) ? Compute an estimate of overdispersion (sum(residuals(model,type="pearson")^2), and and see http://glmm.wikidot.com/faq ? Ben Bolker
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