jags.fit: Fit JAGS models with cloned data

Description

Convenient functions designed to work well with cloned data arguments and JAGS.

Usage

jags.fit(data, params, model, inits = NULL, n.chains = 3, 
    n.adapt = 1000, n.update = 1000, thin = 1, n.iter = 5000, 
    updated.model = TRUE, ...)

Value

An mcmc.list object. If data cloning is used via the data argument, summary returns a modified summary containing scaled data cloning standard errors (scaled by sqrt(n.clones), see dcsd), and \(R_{hat}\) values (as returned by gelman.diag).

Arguments

data: A named list or environment containing the data. If an environment, data is coerced into a list.
params: Character vector of parameters to be sampled.
model: Character string (name of the model file), a function containing the model, or a or custommodel object (see Examples).
inits: Optional specification of initial values in the form of a list or a function (see Initialization at jags.model). If NULL, initial values will be generated automatically. It is an error to supply an initial value for an observed node.
n.chains: Number of chains to generate.
n.adapt: Number of steps for adaptation.
n.update: Number of updates before iterations. It is usually a bad idea to use n.update=0 if n.adapt>0, so a warning is issued in such cases.
thin: Thinning value.
n.iter: Number of iterations.
updated.model: Logical, if the updated model should be attached as attribute (this can be used to further update if convergence was not satisfactory, see updated.model and update.mcmc.list).
...: Further arguments passed to coda.samples, and update.jags (e.g. the progress.bar argument).

Author

Peter Solymos, solymos@ualberta.ca

Examples

Run this code

if (FALSE) {
if (require(rjags)) {
## simple regression example from the JAGS manual
jfun <- 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)
## list of data for the model
jdata <- list(N = N, Y = Y, x = x)
## what to monitor
jpara <- c("alpha", "beta", "sigma")
## fit the model with JAGS
regmod <- jags.fit(jdata, jpara, jfun, n.chains = 3)
## model summary
summary(regmod)
## data cloning
dcdata <- dclone(jdata, 5, multiply = "N")
dcmod <- jags.fit(dcdata, jpara, jfun, n.chains = 3)
summary(dcmod)
}
}