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.
