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


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On Mon, Aug 10, 2015 at 1:37 PM, Steve Walker <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

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