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 ______________________________________________ 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.
