search for: vanamelie

...e suitable for your query. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Mar 4, 2018 at 4:10 AM, Vaniscotte Am?lie <vanamelie at gmail.com> wrote: > Dear R users, > > > I am using the *mgcv package* to model the ratio of hectares of damaged > culture by wild boar in french departments according to some > environmental covariates. I used a _Beta distribution_ for the response. > > > For each...

Dear R users, I am using the *mgcv package* to model the ratio of hectares of damaged culture by wild boar in french departments according to some environmental covariates. I used a _Beta distribution_ for the response. For each department, we estimated the damaged in 3 different culture types (??Culture??). Our statistical individual are therefore the department crossed by the culture

...so if you find out anything I'd be happy to hear more about it! Based on what I've found so far it seems like doing a multinomial glmm is not very straightforward, unfortunately... Best, Steve Politzer-Ahles Message: 94 > Date: Wed, 12 Sep 2012 09:47:12 +0200 > From: Vaniscotte <vanamelie@gmail.com> > To: r-help@r-project.org > Subject: [R] multinomial MCMCglmm > Message-ID: <50503E00.4060600@gmail.com> > Content-Type: text/plain; charset=ISO-8859-1; format=flowed > > Dear all, > > I would like to add mixed effects in a multinomial model and I am try...

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl