[R] glmmBUGS: logistic regression on proportional data

[David Winsemius]

On Feb 8, 2009, at 10:46 AM, Dieter Menne wrote:

> John Poulsen <jpoulsen <at> zoo.ufl.edu> writes:
>
>> I am trying to run a logistic regression with random effects on
>> proportional data in glmmBUGS.  I am a newcomer to this package, and
>> wondered if anyone could help me specify the model correctly.
>>
>> I am trying to specify the response variable, /yseed/, as # of  
>> successes
>> out of total observations... but I suspect that given the error  
>> below,
>> that is not correct.  Also, Newsect should be a factor, whereas  
>> Newdist
>> is continuous.
>>
>> Thanks,
>> John
>>
>> Newdat<-data.frame(Newtree=rep(1:3, each=20), Newsect=rep(c("a","b"),
>> each=10), Newdist=rep(1:5, 2),
>>                   y=rpois(60,2), tot=rep(c(14,12,10,8,6), 12))
>>
>> yseed<-cbind(Newdat$y, Newdat$tot)
>>
>> mod<-glmmBUGS(yseed~Newsect + Newdist, effects="Newtree",
>> family="binomial", data=Newdat)
>>
>
> First, a typo, there is no yseed. Second, after the error message
> "must be between 0 and 1", this looks more like poisson, because
> you have the counts, not the events.

Puzzled. I see yseed defined above as a two column vector, as is  
sometimes used to handle grouped data input to the glm response side  
of a formula.

>
>
> This might come close
>
> mod<-glmmBUGS(y~Newsect + Newdist, effects="Newtree",
>  family="poisson", data=Newdat)

Reasoning only by analogy from the experience with ordinary glm()  
input to create a Poisson model and having no experience with glmmBUGS:
  How you are accounting for the tot (presumably totals) from which it  
appears the y variable is being considered as forming a proportion?  
Would have expected to see an offset=log(tot) or perhaps a weights=tot  
in that call.



>
>
>
> Dieter
>
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