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.
