Alright, I feel stupid now. That was the problem. For glm you can use both successes and failures, while with the negative binomial it is simply a count. That is why I was getting the subscript too long message. I understand generalized linear models, but I haven't worked with negative binomial distribution models, so I will read up on it before I try to use it. RTFM, right?
I will read about using betabinomial for overdispersed data. Thanks for your help, Wade On Thu, Apr 3, 2008 at 8:30 AM, Michael Dewey <[EMAIL PROTECTED]> wrote: > At 12:54 03/04/2008, 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. > > > > For Poisson models and for the negative binomial you have a single > outcome, a count. > For the binomial you can have two columns of counts of successes and > failures (there are other ways of arranging your data). > > I think you might want to try the beta-binomial which is available I think > in aod. > > However I still think reading the relevant section of MASS first would be > a good idea (or some equivalent text). > > > Wade > > > > On Thu, Apr 3, 2008 at 7:23 AM, Michael Dewey <<mailto: > > [EMAIL PROTECTED]>[EMAIL PROTECTED]> wrote: > > 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>http://www.aghmed.fsnet.co.uk > > > > > Michael Dewey > http://www.aghmed.fsnet.co.uk > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
