[R] Regression Overdispersion?

[JvanDyne]

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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