R help - Apr 2008 - Overdispersion in count data

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.

Needless to say that I am at the outer limits of my statistical knowledge.

Thanks for any help,

Wade Wall

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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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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