Greetings R Users! I have a data set of count responses for which I have made repeated observations on the experimental units (stream reaches) over two air photo dates, hence the mixed effect. I have been using Dr. Jim Lindsey's GLMM function found in his "repeated" measures package with the "poisson" family. My problem though is that I don't think the poisson distribution is the right one to discribe my data which is overdispersed; the variance is greater than the mean. I have read that the "negative binomial" regression models can account for some of the differences among observations by adding in a error term that independent of the the covariates. I haven't yet come across a mixed effects model that can use the "negative binomial" distribution. If any of you know of such a function - I will certainly look forward to hearing from you! Additionally, if any of you have insight on zero-inflated data, and testing for this, I'd be interested in your comments too. I'll post a summary of your responses to this list. Best Regards, Nadele Flynn, M.Sc. candidate. University of Alberta
On Tue, 5 Apr 2005 11:20:37 -0600 nflynn at ualberta.ca wrote:> Greetings R Users! > > I have a data set of count responses for which I have made repeated > observations on the experimental units (stream reaches) over two air > photo dates, hence the mixed effect. I have been using Dr. Jim > Lindsey's GLMM function found in his"repeated" measures package with > the "poisson" family. > > My problem though is that I don't think the poisson distribution is > the right one to discribe my data which is overdispersed; the variance > is greater than the mean. I have read that the "negative binomial" > regression models can account for some of the differences among > observations by adding in a error term that independent of the the > covariates.glm.nb() from package MASS fits negative binomial GLMs.> I haven't yet come across a mixed effects model that can use the > "negative binomial" distribution.For known theta, you can plug negative.binomial(theta) into glmmPQL() for example. (Both functions are also available in MASS.) I'm not sure whether there is also code available for unknown theta.> If any of you know of such a function - I will certainly look forward > to hearing from you! Additionally, if any of you have insight on > zero-inflated data, and testing for this, I'd be interested in your > comments too. I'll post a summary of your responses to this list.Look at package zicounts for zero-inflated Poisson and NB models. For these models, there is also code available at http://pscl.stanford.edu/content.html which also hosts code for hurdle models. hth, Z> Best Regards, > Nadele Flynn, M.Sc. candidate. > University of Alberta > > ______________________________________________ > 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 >
Check out these recent postings to the R list: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48429.html http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48646.html Cheers, Pierre nflynn at ualberta.ca wrote:> Greetings R Users! > > I have a data set of count responses for which I have made repeated observations > on the experimental units (stream reaches) over two air photo dates, hence the > mixed effect. I have been using Dr. Jim Lindsey's GLMM function found in his > "repeated" measures package with the "poisson" family. > > My problem though is that I don't think the poisson distribution is the right > one to discribe my data which is overdispersed; the variance is greater than > the mean. I have read that the "negative binomial" regression models can > account for some of the differences among observations by adding in a error > term that independent of the the covariates. > > I haven't yet come across a mixed effects model that can use the "negative > binomial" distribution. > > If any of you know of such a function - I will certainly look forward to hearing > from you! Additionally, if any of you have insight on zero-inflated data, and > testing for this, I'd be interested in your comments too. I'll post a summary > of your responses to this list. > > Best Regards, > Nadele Flynn, M.Sc. candidate. > University of Alberta > > ______________________________________________ > 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 >-- ----------------------------------------------------------------- Pierre Kleiber, Ph.D Email: pkleiber at honlab.nmfs.hawaii.edu Fishery Biologist Tel: 808 983-5399 / (hm)808 737-7544 NOAA Fisheries Service - Honolulu Laboratory Fax: 808 983-2902 2570 Dole St., Honolulu, HI 96822-2396 ----------------------------------------------------------------- "God could have told Moses about galaxies and mitochondria and all. But behold... It was good enough for government work."