[R] an ordinal regression MCMC run high correlation

ping chen chen1984612 at yahoo.com.cn
Tue Mar 16 18:19:03 CET 2010

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) {
c[j]~dnorm(a1, tau)
a1~dnorm(0, 1.0E-06)
theta~dnorm(0, 1.0E-06)I(0,)


Thanks, Ping

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