[R] Regression Overdispersion?

[David Winsemius]

On Feb 1, 2015, at 8:26 AM, JvanDyne 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 
> 

This is possibly an interesting question, but at the moment it is both off-topic on R and probably deserving of a book chapter as an answer. There are simply no specifics. One place where it would be on-topic and if tightened up with a specific example might prompt interesting and useful answers from a knowledgeable audience would be http://CrossValidated.com .


> 
> Sent from the R help mailing list archive at Nabble.com.

The Nabble "archive" of R-help is neither an archive of any sort since they arbitraily delte postings and not is most certainly not "the" Rhelp archive.

Maybe if I unquote this four line message, then Nabble users will see it, although usually it get s trimmed:

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-- 

David Winsemius
Alameda, CA, USA