Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which is what I assume the quasi- distributions do). Thank you for any input at all. Hank Dr. Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/~stevenmh/ http://www.muohio.edu/ecology/ http://www.muohio.edu/botany/ "E Pluribus Unum"
"Martin Henry H. Stevens" <HStevens at muohio.edu> writes:> Hello all: > I frequently have glm models in which the residual variance is much > lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF > = 82). Is it appropriate for me to use a quasipoisson error > distribution and test it with an F distribution? It seems to me that > I could stand to gain a much-reduced standard error if I let the > procedure estimate my dispersion factor (which is what I assume the > quasi- distributions do). > > Thank you for any input at all.I don't think it is safe to say anything general about this without knowledge of the model and the subject matter. Residual deviances can be terribly misleading. Consider for instance this: y <- c(0,1); w <- c(50,50) summary(glm(y~1, binomial, weights=w)) y1 <- .5; w1 <- 100 summary(glm(y1~1, binomial, weights=w1)) Notice that coeff. and s.e. is exactly the same, but not the residual deviances. Now, in the first case, did the zeros and ones sort themselves into two completely separated groups, or was that just because data was given pre-tabulated? -- O__ ---- Peter Dalgaard ??ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
Martin Henry H. Stevens wrote:> Hello all: > I frequently have glm models in which the residual variance is much > lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF > = 82). Is it appropriate for me to use a quasipoisson error > distribution and test it with an F distribution? It seems to me that > I could stand to gain a much-reduced standard error if I let the > procedure estimate my dispersion factor (which is what I assume the > quasi- distributions do). >I did'nt see an answer to this. maybe you could treat as a quasimodel, but first you should ask why there is underdispersion. Underdispersion could arise if you have dependent responses, for instance, competition (say, between plants) could produce underdispersion. Then you would be better off changing to an appropriate model. maybe you could post more about your experimental setup? Kjetil> Thank you for any input at all. > > Hank > > Dr. Martin Henry H. Stevens, Assistant Professor > 338 Pearson Hall > Botany Department > Miami University > Oxford, OH 45056 > > Office: (513) 529-4206 > Lab: (513) 529-4262 > FAX: (513) 529-4243 > http://www.cas.muohio.edu/~stevenmh/ > http://www.muohio.edu/ecology/ > http://www.muohio.edu/botany/ > "E Pluribus Unum" > > ______________________________________________ > R-help at 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 > >--
On Mon, 10 Oct 2005, Martin Henry H. Stevens wrote:> Hello all: > I frequently have glm models in which the residual variance is much > lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF > = 82). Is it appropriate for me to use a quasipoisson error > distribution and test it with an F distribution? It seems to me that > I could stand to gain a much-reduced standard error if I let the > procedure estimate my dispersion factor (which is what I assume the > quasi- distributions do). > > Thank you for any input at all.This usually indicates a deviation from the large-sample theory because of small counts. See e.g. MASS4 p.208. Then estimator residual variance ----------------- residual degrees of freedom is unreliable. If the better methods discuss there confirm under-dispersion, then you probably have some form of negative correlation and need to look at your experimental setup. (But it is usually are false alarm.) -- 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) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595