[R] Overdispersion in count data
Prof Brian Ripley
ripley at stats.ox.ac.uk
Thu Apr 3 14:28:13 CEST 2008
On Thu, 3 Apr 2008, Wade Wall wrote:
> That is exactly how I am writing it. Glm works fine, but as I stated the
> residual deviance is much greater (10x) than the degrees of freedom. I want
> to take a look at using the negative binomial distribution, but I can't get
> glm.nb to work. I get the message Error: (subscript) logical subscript too
> long. I have used traceback() and it seems to be in the glm.fitter
> function, but as I say I am at the limit of my abilities here.
But we can only help you debug this if we have a reproducible example.
> On Thu, Apr 3, 2008 at 7:23 AM, Michael Dewey <info at aghmed.fsnet.co.uk>
>> 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
>>> (category) since burn. However, the residual deviance is roughly 10
>>> the number of degrees of freedom, and using the quasibinomial
>>> doesn't change this. Any suggestions as to why the quasibinomial
>>> distribution doesn't change the residual deviance and how I should
>>> 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
>>> Thanks for any help,
>>> Wade Wall
>>> [[alternative HTML version deleted]]
>> Michael Dewey
> [[alternative HTML version deleted]]
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Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
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