[R] Overdispersion in count data

[Gavin Simpson]

On Wed, 2008-04-02 at 12:03 -0400, 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.

The quasi families estimate the dispersion parameter rather than assume
it is fixed. This doesn't change the estimates for the coefficients, but
it may change their standard errors if the estimated dispersion
parameter is different from 1, and hence the test statistics and their
p-values. As such the residual deviance doesn't change, you are just
adjusting the interpretation of coefficients to take account of the
over-dispersion.

If you are not happy with the fitted model there are numerous options
you could try, including fitting a negative binomial (NB) GLM (see
glm.nb() in package MASS) or a zero-inflated Poisson or NB model or a
Hurdle model. Functions to fit the ZIP/ZINB or Hurdle models can be
found in the pscl package.

HTH

G

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