bayescount (version 0.8.2)

run.jags: RUN A USER SPECIFIED MODEL IN JAGS FROM WITHIN R

Description

Allows any user specified model to be run in JAGS, with the MCMC output returned as a list of MCMC objects for each chain and the end state of each chain. 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). 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(data=stop("No data supplied"), model=stop("No model supplied"), 
   inits = stop("No inital values supplied"), 
   monitor = stop("No monitored variables supplied"), 
   burnin = 5000, updates = 10000, jags=findjags(), 
   silent.jags = FALSE, check.conv=TRUE)

Value

The results of the simulation are returned as a list of MCMC objects, followed by a description of the model state in the R dump format at the last iteration. An MCMC object and model state description are returned for each chain. Model state descriptions can be used as an initial value string for a continuing simulation.

See Also

bayescount bayescount.single dump.format run.model testjags findjags

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(c("x", "y", "n"), list(x, y, length(x)))
inits1 <- dump.format(c("m", "c", "precision"), list(1, 1, 1))
inits2 <- dump.format(c("m", "c", "precision"), list(0.1, 10, 1))

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

# Analyse the results
m <- summary(c(results[[1]][,1], results[[2]][,1]))
c <- summary(c(results[[1]][,2], results[[2]][,2]))
p <- summary(c(results[[1]][,3], results[[2]][,3]))