[R] glmmBUGS: logistic regression on proportional data
dwinsemius at comcast.net
Sun Feb 8 18:17:46 CET 2009
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
>> out of total observations... but I suspect that given the error
>> that is not correct. Also, Newsect should be a factor, whereas
>> is continuous.
>> 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.
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