[R] Regression Overdispersion?

[David Barron]

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 <e283851 at 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
>
>
>
>
>
> --
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>
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