runjags (version 0.9.0-4)

run.jags: Run a User Specified Bayesian MCMC Model in JAGS from Within R

Description

Runs a user specified JAGS (similar to WinBUGS) model from within R, returning a list of the MCMC chain(s) along with convergence diagnostics, autocorrelation diagnostics and monitored variable summaries. Data and initial values must be supplied in the R dump format (see dump.format() for an easy way to do this). A character vector of variables to monitor must also be supplied. Requires Just Another Gibbs Sampler (JAGS), see http://www-fis.iarc.fr/~martyn/software/jags/. The GUI interface for R in Windows may not continually refresh the output window, making it difficult to track the progress of the simulation (if silent.jags is FALSE). To avoid this, you can run the function from the terminal version of R (located in the Program Files/R/bin/ folder).

Usage

run.jags(model=stop("No model supplied"), 
   monitor = stop("No monitored variables supplied"),
   data = NA, n.chains = 2, inits = replicate(n.chains, NA),
   burnin = 5000, sample = 10000, 
   adapt=if(burnin

Arguments

model

a character string of the model in the JAGS language. No default.

monitor

a character vector of the names of variables to monitor. No default.

data

a character string in the R dump format (or a named list) containing the data. If left as NA, no external data is used in the model. Default NA.

n.chains

the number of chains to use for the simulation. Must be a positive integer. Default 2.

inits

a character vector with length equal to the number of chains the model will be run using. Each element of the vector must be a character string in the R dump format representing the initial values for that chain, or NA. If not all initialisable variable

burnin

the number of burnin iterations (not sampled) to use (numeric). Default 5000 iterations.

sample

the number of sampling iterations to use (numeric). Default 10000 iterations.

adapt

advanced option to control the length of the adaptive phase directly, which is otherwise half the length of the burnin period. Default is 0, unless burnin is less than 200 in which case 100 adapitve iterations are used.

jags

the system call or path for activating JAGS. Default calls findjags() to attempt to locate JAGS on your system.

silent.jags

should the JAGS output be suppressed? (logical) If TRUE, no indication of the progress of individual models is supplied. Default FALSE.

check.conv

should the convergence be assessed after the model has completed? If TRUE, each monitored variable will be assessed for a potential scale reduction factor of the Gelman Rubin statistic of less than 1.05, which indicates adequate convergence. 2 or more c

Value

• The results of the simulation are returned as list including the following items (omitting the last 3 items if check.conv==FALSE):
• mcmc
  an MCMC list of MCMC objects representing the chains
• end.state
  a character vector of length equal to the number of chains representing a description of the model state in the R dump format for each chain at the last iteration. This can be used as an initial values vector to restart a new simulation in the same place as the previous simulation ended.
• burnin
  number of burnin iterations discarded before sampling.
• sample
  number of sampled iterations.
• summary
  the summary of each monitored variable (equivalent to summary(mcmc)).
• psrf
  the output of the Gelman Rubin dignostic (equivalent to gelman.diag(mcmc)).
• autocorr
  the output of the autocorrelation diagnostic (equivalent to autocorr.diag(mcmc)).

See Also

autorun.jags run.jagsfile testjags dump.format summary.mcmc gelman.diag autocorr.diag

Examples

# run a model to calculate the intercept and slope of the expression y = m x + c, assuming normal observation errors for y:

# Simulate the data
X <- 1:100
Y <- rnorm(length(X), 2*X + 10, 1)

# Model in the JAGS format
model <- "model {
for(i in 1 : N){
Y[i] ~ dnorm(true.y[i], precision);
true.y[i] <- (m * X[i]) + c;
}
m ~ dunif(-1000,1000);
c ~ dunif(-1000,1000);
precision ~ dexp(1);
}"

# Use dump.format to convert the data and initial values files into the R dump format
data <- dump.format(list(X=X, Y=Y, N=length(X)))
inits1 <- dump.format(list(m=1, c=1, precision=1))
inits2 <- dump.format(list(m=0.1, c=10, precision=1))

# Run the model
results <- run.jags(model=model, monitor=c("m", "c", "precision"), data=data, n.chains=2, inits=c(inits1,
inits2))

# Analyse the results
results$summary