parCodaSamples: Generate posterior samples in 'mcmc.list' format on parallel workers

Description

This function sets a trace monitor for all requested nodes, updates the model on each workers. Finally, it return the chains to the master and coerces the output to a single mcmc.list object.

Usage

parCodaSamples(cl, model, variable.names, n.iter, thin = 1, na.rm = TRUE, ...)

Value

An mcmc.list object with possibly an n.clones attribute.

Arguments

cl: A cluster object created by makeCluster, or an integer. It can also be NULL, see parDosa.
model: character, name of a jags model object
variable.names: a character vector giving the names of variables to be monitored
n.iter: number of iterations to monitor
thin: thinning interval for monitors
na.rm: logical flag that indicates whether variables containing missing values should be omitted. See details in help page of coda.samples.
...: optional arguments that are passed to the update method for jags model objects

Author

Peter Solymos, solymos@ualberta.ca

Examples

Run this code

if (FALSE) {
if (require(rjags)) {
model <- function() {
    for (i in 1:N) {
        Y[i] ~ dnorm(mu[i], tau)
        mu[i] <- alpha + beta * (x[i] - x.bar)
    }
    x.bar <- mean(x[])
    alpha ~ dnorm(0.0, 1.0E-4)
    beta ~ dnorm(0.0, 1.0E-4)
    sigma <- 1.0/sqrt(tau)
    tau ~ dgamma(1.0E-3, 1.0E-3)
}
## data generation
set.seed(1234)
N <- 100
alpha <- 1
beta <- -1
sigma <- 0.5
x <- runif(N)
linpred <- crossprod(t(model.matrix(~x)), c(alpha, beta))
Y <- rnorm(N, mean = linpred, sd = sigma)
jdata <- list(N = N, Y = Y, x = x)
jpara <- c("alpha", "beta", "sigma")
## jags model on parallel workers
## n.chains must be <= no. of workers
cl <- makePSOCKcluster(4)
parJagsModel(cl, name="res", file=model, data=jdata,
    n.chains = 2, n.adapt=1000)
parUpdate(cl, "res", n.iter=1000)
m <- parCodaSamples(cl, "res", jpara, n.iter=2000)
stopifnot(2==nchain(m))
## with data cloning
dcdata <- dclone(list(N = N, Y = Y, x = x), 2, multiply="N")
parJagsModel(cl, name="res2", file=model, data=dcdata,
    n.chains = 2, n.adapt=1000)
parUpdate(cl, "res2", n.iter=1000)
m2 <- parCodaSamples(cl, "res2", jpara, n.iter=2000)
stopifnot(2==nchain(m2))
nclones(m2)
stopCluster(cl)
}
}