Actually, glm() does not estimate the dispersion at all, so you will need to be more specific.
For example, summary.glm() and predict.glm() use the Pearson statistic if dispersion=NULL (the default) for most families. You can supply any other value you choose, and the MASS package makes use of this for ML estimation of the dispersion parameter (related to the shape) of the gamma family. There are rather good reasons (serious bias) not to use the deviance estimate in the binomial and Poisson families (see the example plots in MASS4), and good reasons not to use either in the gamma family. As the Pearson and deviance estimates agree for the gaussian, that does leave begging the question of why you want to do this. Further, McCullagh & Nelder have general arguments why the Pearson estimate might always be preferred to the deviance one. So that `another statastical package' appears to need justification for its choice. On Mon, 11 Jul 2005, Smit, R. (Robin) wrote: > The estimate of glm dispersion can be based on the deviance or on the > Pearson statistic. > I have compared output from R glm() to another statastical package and > it appears that R uses the Pearson statistic. > I was wondering if it is possible to make use R the deviance instead by > modifying the glm(...) function? > Thanks for your attention. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
