dclone (version 2.1-1)

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, ...)

Arguments

cl

A cluster object created by makeCluster, or an integer. It can also be NULL, see snowWrapper.

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

...

optional arguments that are passed to the update method for jags model objects

Value

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

See Also

Original sequential function in rjags: coda.samples

Sequential dclone-ified version: codaSamples

Examples

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)
}