codaSamples: Generate posterior samples in mcmc.list format

Description

This function sets a trace monitor for all requested nodes, updates the model and coerces the output to a single mcmc.list object. This function uses coda.samples but keeps track of data cloning information supplied via the model argument.

Usage

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

Value

An mcmc.list object. An n.clones attribute is attached to the object, but unlike in jags.fit there is no

updated.model attribute as it is equivalent to the input jags model object.

Arguments

model: 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) {
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 <- dclone(list(N = N, Y = Y, x = x), 2, multiply="N")
jpara <- c("alpha", "beta", "sigma")
## jags model
res <- jagsModel(file=model, data=jdata, n.chains = 3, n.adapt=1000)
nclones(res)
update(res, n.iter=1000)
nclones(res)
m <- codaSamples(res, jpara, n.iter=2000)
summary(m)
nclones(m)
}