[R-sig-ME] Mixed model for count data with overdispersion
Mehdi Abedi
abedimail at gmail.com
Mon Aug 10 15:29:15 CEST 2015
Thanks Paul and Steve,
If i understood well you mean somethings like this!?:
library( glmmADMB)
Model2<- glmmadmb(Totalseedling~Heatsmoke *Cold+(1|Plots),data=growthdata,
family="nbinom1")
summary(Model2)
Is it possible to get only main effect results with some function like
anova or similar in glmmADMB. I am mainly interested to only main effect(
Heatsmoke and Cold) than theirs levels results. I think anova function
doesn't play role in these advanced package :)
All the best,
Mehdi
On Mon, Aug 10, 2015 at 5:15 PM, Paul Johnson <paul.johnson at glasgow.ac.uk>
wrote:
> 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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> <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>
> 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
> >>
> >>
> >>
> >
> >
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>
--
*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*
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