[R] Regression Overdispersion?

[Rune Haubo]

A third, and often preferable, way is to add an observation-level random effect:

library(lme4)
data1$obs <- factor(seq_len(nrow(data1)))
model <- glmer(y ~ x1 + x2 + (1 | obs), family=poisson(link=log), data=data1)

See http://glmm.wikidot.com/faq and search for "individual-level
random effects".

Cheers,
Rune

On 1 February 2015 at 19:55, David Barron <dnbarron at gmail.com> wrote:
> 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
>>
>>
>>
>>
>>
>> --
>> 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.
>>
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