Solved – How to identify this trap error in WinBUGS

bayesiansurvivalwinbugs

I am currently working on my thesis and interested in estimating a Weibull conditional Hazard Frailty Model for recurrent event data using WinBUGS. I wrote the model but when am trying to use it with the following script it hangs unexpectedly. It returns a messages titled "undefined real result". What am I doing wrong? Also, I have tried different prior and initial values but I could not solve the problem!

model
{
for(i in 1:633){
ht[i]<-gama*pow((t[i]),(gama-1))*exp(age[i]*b[1]+sex[i]*b[2]+mar[i]*b[3]+back1[i]*b[4]+back2[i]*b[5]+form[i]*b[6]+u[subject[i]]+delta*enu[i])
}
for(i in 1:633){
beta[i]<-pow((1/gama),((gama*pow((t[i]),(gama-1)))/ht[i]))
}
for(i in 1:633){
t[i]~dweib(gama,beta[i]) I(cen[i],)
}
for (j in 1:6){
b[j]~dnorm(0,0.001)
}
for(i in 1:159){
u[i]~dnorm(0,tau)
}
delta~dnorm(0,0.01)
gama~dgamma(1,0.01)
tau~dunif(0,100)
sigma2.subject<-1/tau
}
list(b=c(0,0,0,0,0,0),gama=1,tau=1,delta=0)

I would appreciate it if you could help me for solving this problem.

Best Answer

This is most likely due to numerical overflow, which is also stated in this troubleshooting guide for Bugs.

'undefined real result' indicates numerical overflow. Possible reasons include:
- initial values generated from a 'vague' prior distribution may be numerically extreme - specify appropriate initial values;
- numerically impossible values such as log of a non-positive number - check, for example, that no zero expectations have been given when Poisson modelling;
- numerical difficulties in sampling. Possible solutions include:
- better initial values;
- more informative priors - uniform priors might still be used but with their range restricted to plausible values;
- better parameterisation to improve orthogonality;
- standardisation of covariates to have mean 0 and standard deviation 1.
- can happen if all initial values are equal.

So there are a number of different things to consider, and we cannot evaluate problems in your data. Since you have tried different initial values this can maybe be ruled out. I would suggest that you look closely at your data and maybe try to standardise variables.

Have you checked the data for problems? For instance, you are using beta[i] as a parameter in the Weibull distribution, and from what I know this must be >0. If any value in beta[i] is zero this might cause this error.

In your model, one if the definitions is t[i]~dweib(gama,beta[i]) I(cen[i],), which looks a bit strange and is probably a typo. What is I(cen[i],) defining? It shouldn't be the reason for this particular error though.

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