an undefined ancestor node or a directed cycle

[Jun Xu]

Dear all,

I am kind of new to JAGS, and I am trying to estimate a random intercept logit model. But somehow I am still getting this error message after debug:

Error in jags.model("model.txt", data = dataList, n.chains = nChains, :
RUNTIME ERROR:
Compilation error on line 4.
Unable to resolve node mu[1]
This may be due to an undefined ancestor node or a directed cycle in the graph

I believe I've defined all parameters....Here are r and rjags codes to estimate a simplified debuggable version of the model. Also the group variable countryR has been sorted and recoded into 1-33 in Stata before data being read into R.

require(foreign)
require(rjags)         

modelstring = "
model {
  for( i in 1 : nData ) {
    hlthprc2[i] ~ dbern(mu[i])
    logit(mu[i]) <- b0[countryR[i]] + b1*age[i]
  }

  b[1] ~ dnorm(0 , 1.0E-12 )

  for ( k in 1:nGroup) {
    b0[k] ~ dnorm(b0.mean, b0.tau)
  } 

  b0.mean ~ dnorm(0, 1.0E-4) 
  b0.tau ~ dgamma(1.0E-3, 1.0E-3)

}
" 
writeLines(modelstring,con="model.txt")

dataSource = read.dta("data/issp2007Bayes10pct.dta", convert.factor=FALSE)

dataMat = as.matrix(dataSource)
nData = NROW(dataMat)

hlthprc2 = as.matrix(dataMat[,1])
age = as.matrix(dataMat[,2])
countryR = as.matrix(dataMat[,13])
nGroup = length(unique(dataSource$countryR))

dataList = list('hlthprc2' = as.vector( hlthprc2 ) , 
                'age' = as.vector(age),         
                'countryR' = as.vector(countryR),
                'nGroup' = nGroup,
                'nData' = nData
                )

parameters = c( "b0" , "b1")  
adaptSteps = 1000              
burnInSteps = 2000            
nChains = 3                   
numSavedSteps=50000           
thinSteps=1                   
nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
# Create, initialize, and adapt the model:
jagsModel = jags.model( "model.txt" , data=dataList , 
                        n.chains=nChains , n.adapt=adaptSteps )
# Burn-in:
cat( "Burning in the MCMC chain...\n" )
update( jagsModel , n.iter=burnInSteps )
# The saved MCMC chain:
cat( "Sampling final MCMC chain...\n" )
codaSamples = coda.samples( jagsModel , variable.names=parameters , 
                            n.iter=nPerChain , thin=thinSteps )
mcmcChain = as.matrix( codaSamples )

[Jun Xu]

so careless of me. Here b[1] should be b1.