I am trying to model a clusterd ordinal response data (either 1, 2 or 3) called
, the correponding physician of the patient is also in the data.
Since it is ordinal, I used the ordinal logit model
topbox[i]~discrete with probability P[j,1],p[j,2], p[j,3], j is the
corresponding physician of the ith patient
C[j] is the physician effect , a1 and a1+theta is the common cutpoints for all
physicians
I generate 10,000 iteration and there are still high autocorrelation of a1 and
tau. I thought 10,000 is a pretty big number and the chain converges really
slow. I am a new MCMC user and don't know other ways to solve this problem.
Will someone please give some suggestions that may apply to this specific
modeling?
model {
for ( i in 1:N) {
response[i]~dcat( p[physician[i], ] )
}
for (j in 1:Nt) {
p[j,1]<-1-Q[j,1]
p[j,2]<-Q[j,1]-Q[j,2]
p[j,3]<-Q[j,2]
logit(Q[j,1])<--c[j]
logit(Q[j,2])<--(c[j]+theta)
score[j]<-0.5*p[j,2]+p[j,3]
c[j]~dnorm(a1, tau)
}
a1~dnorm(0, 1.0E-06)
theta~dnorm(0, 1.0E-06)I(0,)
tau~dgamma(0.001,0.001)
}
list(N=667,Nt=50)
Thanks, Ping
