library(BAYSTAR)
data(unemployrate)
x  <- unemployrate
nx <- length(x)
dif.x <- x[2:nx]-x[2:nx-1]
lagp1 <- c(2,3,4,10,12)
lagp2 <- c(2,3,12)

## Total MCMC iterations and burn-in iterations
Iteration <- 10000
Burnin    <- 2000

## A RW (random walk) MH algorithm is used in simulating the threshold value
## Step size for the RW MH

step.thv<- 2.5

out <- BAYSTAR(dif.x,lagp1,lagp2,Iteration,Burnin,constant=0,step.thv=step.thv)

# Geweke's Convergence Diagnostic
library(coda)
geweke.diag(out$posterior, frac1=0.1, frac2=0.5)

## Model Comparison. Assume maximum lag d0 = 4 now
d0 <- 4
out2 <- BAYSTAR(dif.x,lagp1,lagp2,Iteration,Burnin,d0,constant=0,step.thv=step.thv)


geweke.diag(out2$posterior, frac1=0.1, frac2=0.5)