Question

I use my JAGS model, and then use that model to make predictions, propogating parameter uncertainty into those predictions. I currently have a zero-inflated Poisson (ZIP) model that runs fine making a single prediction. It looks like this:

j.model ="
model{
for (i in 1:N){
#this fits the blended model to your data. 
y[i] ~ dpois(m[i]*t[i])

#This blends the poisson and zero inflation models
m[i] <- mu[i]*x[i] + 0.00001

#this is the bernoulli outcome of the zero inflation
x[i] ~ dbern(pro[i])

#this logit transforms the theta model for 0-1 probability of zero inflation
logit(pro[i]) <- theta[i]

#mu[i] is predictors for poisson model- log link function.
log(mu[i]) <- int.p + x1[i]*b1.p + x2[i]*b2.p + x3[i]*b3.p + x4[i]*b4.p + x5[i]*b5.p

#theta[i] is predictors for zero inflation
theta[i]   <- int.z + x1[i]*b1.z + x2[i]*b2.z + x3[i]*b3.z + x4[i]*b4.z + x5[i]*b5.z
}

#predictions
#This gets the range of pred.m blended model values, before they get sent through the poisson distribution, and corrected for time. 

#x5 prediction
for (i in 1:N.pred){
x5.pred[i] <- pred5.mu[i]*pred5.x[i] + 0.00001
pred5.x[i] ~ dbern(pred5.pro[i])
logit(pred5.pro[i]) <- pred5.theta[i]
log(pred5.mu[i]) <- int.p + x1.m*b1.p + x2.m*b2.p + x3.m*b3.p + x4.m*b4.p + x5.r[i]*b5.p 
pred5.theta[i]   <- int.z + x1.m*b1.z + x2.m*b2.z + x3.m*b3.z + x4.m*b4.z + x5.r[i]*b5.z 
}

#priors
b1.p ~ dnorm(0, .0001)
b2.p ~ dnorm(0, .0001)
b3.p ~ dnorm(0, .0001)
b4.p ~ dnorm(0, .0001)
b5.p ~ dnorm(0, .0001)
b1.z ~ dnorm(0, .0001)
b2.z ~ dnorm(0, .0001)
b3.z ~ dnorm(0, .0001)
b4.z ~ dnorm(0, .0001)
b5.z ~ dnorm(0, .0001)
int.p ~ dnorm(0, .0001)
int.z ~ dnorm(0, .0001)
}
"

However, if I insert a second prediction loop into this model for predictor x4 I get an error. The second prediction loops looks like this, and is inserted between the model loop and the x5 prediction loop.

#x4 prediction
for (i in 1:N.pred){
  map.pred[i] <- pred4.mu[i]*pred4.x[i] + 0.00001
  pred4.x[i] ~ dbern(pred4.pro[i])
  logit(pred4.pro[i]) <- pred4.theta[i]
  log(pred4.mu[i]) <- int.p + x1.m*b1.p + x2.m*b2.p + x3.m*b3.p + x4.r[i]*b4.p + x5.m*b5.p 
  pred4.theta[i]   <- int.z + x1.m*b1.z + x2.m*b2.z + x3.m*b3.z + x4.r[i]*b4.z + x5.m*b5.z 
}

It returns this error:

  RUNTIME ERROR:
Compilation error on line 20.
Unable to resolve node pred4.mu[1]
This may be due to an undefined ancestor node or a directed cycle in the graph

This surprises me, as this prediction loop is formatted exactly the same as the x5 prediction loop, so I know everything should be defined, as it worked great before. Is there something about having two of these that is causing the problem?

directed cycle error in JAGS when making multiple predictions

0 个答案: