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

In dclone: Data Cloning and MCMC Tools for Maximum Likelihood Methods

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.

Author(s)

Peter Solymos, [e-mail]

See Also

Original sequential function in rjags: coda.samples

Sequential dclone-ified version: codaSamples

Examples

## Not run: 
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)
}

## End(Not run)

dclone documentation built on May 2, 2019, 6:08 p.m.