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Results tagged with jags
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user 3098
"JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS." (http://mcmc-jags.sourceforge.net/)
2
votes
1
answer
667
views
Data and model blocks in JAGS
I am trying to model a function of observed data in JAGS. For example
x[i,t]/phi ~ dpois(mu)
where x is observed and phi is a parameter in the model. … I can't seem to get this working and am wondering if I may be missing some larger issue in JAGS. …
- 729
7
votes
Accepted
How does dinterval() for interval censored data work in JAGS?
The deviance will be zero and the "jags" function will fall back on the Gelman heuristic for pD (I did not write this so don't ask me to explain it), which will also be zero. … The likelihood you really want is
p(lim[j,1] < t[j] <= lim[j,2] | mu, tau)
But JAGS is giving you
p(y[j] | t[j])
which is always 1. The "focus" of DIC is wrong. …
- 729
10
votes
1
answer
5k
views
How does dinterval() for interval censored data work in JAGS?
I am trying to understand how dinterval() works in JAGS for censored data. I am modeling coarse data where I only have upper and lower bounds for each data point (not the true value). … ensure the starting value is between the upper and lower limit
#each chain will start at the same place for t but this is just for this case
params <- c("mu","tau")
And run the model:
playmodel.jags <- jags …
- 729
4
votes
Accepted
Is there a way to continue a R/JAGS MCMC chain that did not converge?
You can use autojags(currentmodel,n.iter,...) from R2jags. You can specify the criteria for "convergence" based on $\hat R$.
- 729
0
votes
Accepted
Jags error with dgamma
I have received these errors when not using appropriate initial values for my parameters. Rather than writing functions to generate initial values (which I presume you have done), I would provide sta …
- 729
3
votes
1
answer
1k
views
Autoregressive prior distributions
Here is some jags/bugs code to explain:
model {
#prior on random intercepts
for (p in 1:P){
alpha[p] ~ dnorm(0,0.001)
}
for (d in 2:D){
betastar[d] ~ dnorm(beta[d-1],0.001)
beta[d] ~ dnorm …
- 729
2
votes
1
answer
705
views
Random effect on scale parameter
As you can see in the JAGS code below, I am essentially fitting a accelerated failure time model with interval censored log-normal failure times. …
- 729