dclone (version 2.1-1)

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, ...)

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 a

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

...

Further arguments passed to coda.samples, and update.jags (e.g. the progress.bar argument).

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).

encoding

UTF-8

See Also

Underlying functions: jags.model, update.jags, coda.samples Parallel chain computations: jags.parfit Methods: dcsd, confint.mcmc.list.dc, coef.mcmc.list, quantile.mcmc.list, vcov.mcmc.list.dc

Examples

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)
}