glmmBUGS: logistic regression on proportional data
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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