Dear Bug users:
I am using hierchical modeling for a response varible (ordinal) given by patients for each doctor.
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)
}
The high correlation problem is really obvious.
I am trying to find the reason for the slow convergence.
Can the reason be 20 out of 50 doctors having 5 or less response (6 docs with 1 , 4 docs with 2,? 3 docs with 3, 4 docs with 4 ,2 docs with 5 responses)?
However, small sample sizes are the reason for bayesian modeling in the first place.
Can the reason be there are no strong variation among the doctors to start with? The frequentist test suggests there is no strong variation.
Any thoughts or suggestions will be appreciated.
Ping
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