Edit: I posed this question to Martyn Plummer and got a very useful response. I'll post it once I figure out the details.

One of the hazards of using something like JAGS, with complicated innards written by someone else, is that you don't have much of a chance to the problems coming ahead. I happen to have a continuous process which is observed only in discrete units and it happens to mimic the

cat('

model{

for ( i in 1:N) {

x[i] ~ dunif(0,10^K)

o[i] ~ dinterval(x[i],breaks)

}

}

', file = './dintervalProblem.bugs'

)

library(rjags)

mod <- list()

samp <- list()

timings <- list()

k <- c(3,4,5)

for ( i in 1:length(k) ) {

N <- 100

K <- k[i]

x <- runif(N,0,10^K)

o <- ceiling(x)

breaks <- seq(0,10^K,1)

timings[[i]] <- system.time(

{

mod[[i]] <- jags.model(

file = './dintervalProblem.bugs',

data = list(

N = N,

K = K,

o = o,

breaks = breaks

),

inits = list(

o = o,

x = x

),

n.chains = 1,

n.adapt = 1000,

quiet = FALSE

)

samp[[i]] <- jags.samples(

model = mod1,

variable.names = c('x','o'),

n.iter = 1000,

thin = 1

)

}

)

}

My digging in the JAGS internals hasn't turned up anything yet which could be responsible for this behavior. The value of the dinterval node is calculated by:

It's not like there's a separate node for every breakpoint... though the number of nodes generated in the graphs does go up with K.

Suggestions? (Especially all those folks coming here from Andrew Gelman's blog...)

`dinterval`

distribution in JAGS. Unfortunately I recently had to change the number of breaks used by `dinterval`

by an order of magnitude and it turns out that it bogs down fairly quickly. Here's an example:cat('

model{

for ( i in 1:N) {

x[i] ~ dunif(0,10^K)

o[i] ~ dinterval(x[i],breaks)

}

}

', file = './dintervalProblem.bugs'

)

library(rjags)

mod <- list()

samp <- list()

timings <- list()

k <- c(3,4,5)

for ( i in 1:length(k) ) {

N <- 100

K <- k[i]

x <- runif(N,0,10^K)

o <- ceiling(x)

breaks <- seq(0,10^K,1)

timings[[i]] <- system.time(

{

mod[[i]] <- jags.model(

file = './dintervalProblem.bugs',

data = list(

N = N,

K = K,

o = o,

breaks = breaks

),

inits = list(

o = o,

x = x

),

n.chains = 1,

n.adapt = 1000,

quiet = FALSE

)

samp[[i]] <- jags.samples(

model = mod1,

variable.names = c('x','o'),

n.iter = 1000,

thin = 1

)

}

)

}

My digging in the JAGS internals hasn't turned up anything yet which could be responsible for this behavior. The value of the dinterval node is calculated by:

`static unsigned int value(vector` const &par, unsigned int ncut)

{

double t = T(par);

for (unsigned int i = 0; i < ncut; ++i) {

if (t <= CUTPOINTS(par)[i])

return i;

}

return ncut;

}

It's not like there's a separate node for every breakpoint... though the number of nodes generated in the graphs does go up with K.

Suggestions? (Especially all those folks coming here from Andrew Gelman's blog...)