A third, and often preferable, way is to add an observation-level random effect:
library(lme4) data1$obs <- factor(seq_len(nrow(data1))) model <- glmer(y ~ x1 + x2 + (1 | obs), family=poisson(link=log), data=data1) See http://glmm.wikidot.com/faq and search for "individual-level random effects". Cheers, Rune On 1 February 2015 at 19:55, David Barron <dnbar...@gmail.com> wrote: > There are two straightforward ways of modelling overdispersion: > > 1) Use glm as in your example but specify family=quasipoisson. > 2) Use glm.nb in the MASS package, which fits a negative binomial model. > > > > On 1 February 2015 at 16:26, JvanDyne <e283...@trbvm.com> wrote: >> I am trying to use Poisson regression to model count data with four >> explanatory variables: ratio, ordinal, nominal and dichotomous – x1, x2, x3 >> and x4. After playing around with the input for a bit, I have formed – what >> I believe is – a series of badly fitting models probably due to >> overdispersion [1] - e.g. model=glm(y ~ x1 + >> x2,family=poisson(link=log),data=data1) - and I was looking for some general >> guidance/direction/help/approach to correcting this in R. >> >> [1] – I believe this as a. it’s, as I’m sure you’re aware, a possible reason >> for poor model fits; b.the following: >> >> tapply(data1$y,data$x2,function(x)c(mean=mean(x),variance=var(x))) >> >> seems to suggest that, whilst variance does appear to be some function of >> the mean, there is a consistently large difference between the two >> >> >> >> >> >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/Regression-Overdispersion-tp4702611.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
