[R-sig-ME] Mixed model for count data with overdispersion

[Steve Walker]

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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