Mixed model for count data with overdispersion
I'll second Steve's suggestion (which I think is the easiest, although assessing fit is tricky) and add another suggestion of fitting a negative binomial GLMM in glmmADMB. Paul Sent using CloudMagic<https://cloudmagic.com/k/d/mailapp?ct=pa&cv=7.0.42&pv=4.2.2>
On Mon, Aug 10, 2015 at 1:37 PM, Steve Walker <steve.walker at utoronto.ca<mailto:steve.walker at utoronto.ca>> wrote:
An alternative is to use glmer with `family=Poisson` and an observation-level random effect. I only skimmed this paper, but it will hopefully put you on to the main idea: https://peerj.com/articles/616/ Cheers, Steve
On 2015-08-10 5:27 AM, Mehdi Abedi wrote:
Thanks Chris for lectures, Working with MCMCglmm is like jumping from high school physics to Albert Einstein lectures:). Hopefully i can digest this as a ecologist this modeling part! All the best On Mon, Aug 10, 2015 at 1:46 PM, Christopher David Desjardins < cddesjardins at gmail.com> wrote:
Hi, You really should read about the MCMCglmm package before just using it. There are a couple of vignettes which I strongly suggest that you read prior to actually using MCMCglmm as they explain a lot. https://cran.r-project.org/web/packages/MCMCglmm/vignettes/Overview.pdf https://cran.r-project.org/web/packages/MCMCglmm/vignettes/CourseNotes.pdf Do note that you need to specify prior distributions or at least understand the default ones. Chris On Aug 10, 2015, at 8:56 AM, Mehdi Abedi <abedimail at gmail.com> wrote: Thanks Manabu, It is a bit complicated for me but If i have this data: Parameter: Totalseedling fixed effect: Heatsmoke, cold random effect: plot I should do something like this?! Model1<- MCMCglmm(Totalseedling ~ Heatsmoke *Cold, random = ~Plots,family="poisson", data = growthdata) summary( Model1) It looks i can not get anova() here for output as well? I am not familiar with other details in the MCMCglmm: library( MCMCglmm) Model1<- MCMCglmm(Totalseedling ~ Heatsmoke *Cold, random = ~Plot, + family = "poisson", data = growthdata, prior = prior, + verbose = FALSE, pr = TRUE) Warm regards, Mehdi On Mon, Aug 10, 2015 at 12:48 PM, Manabu Sakamoto < manabu.sakamoto at gmail.com wrote: Dear Mehdi, You can use the function MCMCglmm in the package of the same name, specifying family="poisson". MCMCglmm automatically accounts for over dispersion in count data. best regards, Manabu On 10 August 2015 at 06:54, Mehdi Abedi <abedimail at gmail.com> wrote: Dear all, I had quick search but it looks there is no simple way in lme4 or nlme In the case of overdispersion for count data,. How we can run mixed model for count data with family of quasipoisson or maybe NB? I my working on seeding emergence with 2 fixed factor (n=10) and i would like to have my plot as replicate(n=5) as a random. Warm regards, Mehdi -- *Mehdi Abedi Department of Range Management* *Faculty of Natural Resources & Marine Sciences * *Tarbiat Modares University (TMU) * *46417-76489, Noor* *Mazandaran, IRAN * *mehdi.abedi at modares.ac.ir <Mehdi.abedi at modares.ac.ir>* *Homepage <http://www.modares.ac.ir/en/Schools/nat/Academic_Staff/~mehdi.abedi>* *Tel: +98-122-6253101 * *Fax: +98-122-6253499* [[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models -- Manabu Sakamoto, PhD manabu.sakamoto at gmail.com -- *Mehdi Abedi Department of Range Management* *Faculty of Natural Resources & Marine Sciences * *Tarbiat Modares University (TMU) * *46417-76489, Noor* *Mazandaran, IRAN * *mehdi.abedi at modares.ac.ir <Mehdi.abedi at modares.ac.ir>* *Homepage <http://www.modares.ac.ir/en/Schools/nat/Academic_Staff/~mehdi.abedi>* *Tel: +98-122-6253101 * *Fax: +98-122-6253499* [[alternative HTML version deleted]] _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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