"Wade Wall" <[EMAIL PROTECTED]> wrote in news:[EMAIL PROTECTED]:
> Thanks for the recommendations, insights. I tried using glm.nb, but > it didn't seem to like my data. I received the message (subscript) > logical subscript too long. I am using the same dataframe as my > previous glm. Do you know if I need to put the data in a different > format? I was wondering about your data layout. You said you had the flower/no- flower data in two different columns. That is not the way I usually offer data to glm(). I would have imagined that log(burn_time) would have been an offset. It might help if you at least offered the audience a sample of ten rows, the results of str() for the data.frame, and the call to the glm function. -- David Winsemius > On Wed, Apr 2, 2008 at 12:31 PM, Gavin Simpson > <[EMAIL PROTECTED]> wrote: > >> 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. >> ______________________________________________ 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.
