Re: [R] Overdispersion in count data

From: Michael Dewey <info_at_aghmed.fsnet.co.uk>
Date: Thu, 03 Apr 2008 12:23:40 +0100

At 17:03 02/04/2008, Wade Wall wrote:
>Hi all,
>
>I have count data (number of flowering individuals plus total number of
>individuals) across 24 sites and 3 treatments (time since last burn).
>Following recommendations in the R Book, I used a glm with the model y~
>burn, with y being two columns (flowering, not flowering) and burn the time
>(category) since burn. However, the residual deviance is roughly 10 times
>the number of degrees of freedom, and using the quasibinomial distribution
>doesn't change this. Any suggestions as to why the quasibinomial
>distribution doesn't change the residual deviance and how I should proceed.
>I know that this level of residual deviance is unacceptable, but not sure is
>transformations are in order.

You have received much helpful advice from Gavin and Achim and others but I wonder whether they are answering the quaestion in your title rather than in your post.

Are you doing something like
fit <- glm(cbind(flower, notflower) ~ burn, family = binomial)

You might find it helpful to read the relevant section in MASS (see quasibinomial in the index) or in some other text.

>Needless to say that I am at the outer limits of my statistical knowledge.
>
>Thanks for any help,
>
>Wade Wall
>
> [[alternative HTML version deleted]]

Michael Dewey
http://www.aghmed.fsnet.co.uk



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