library(rjags)
monkey.inits1 <- list(alpha.c=c(0,0,0,0), alpha.tau=1,
		tau.y=1, alpha=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime.c=0, alphaTime.tau=1)

monkey.inits2 <- list(alpha.c=c(0,0,0,0), alpha.tau=1,
		tau.y=1, alpha=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime.c=c(0,0,0,0), alphaTime.tau=1)

monkey.inits2Q <- list(alpha.c=c(0,0,0,0), alpha.tau=1,
		tau.y=1, alpha=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0), 
		alphaTime.c=c(0,0,0,0), alphaTime.tau=1,
		alphaTimeQ.c=c(0,0,0,0), alphaTimeQ.tau=1)

monkey.inits3 <- list(alpha=c(0,0,0,0), 
	        alpha.tau=1,
		tau.y=1, 
		alphaTime=c(0,0,0,0),
		alphaTime.tau=1)
1


monkey.data <- list(time = c(0.0,3.0,6.0,9.0,12.0), tr=c(1,1,1,1,1,2,2,2,3,3,3,3,4,4,4,4),
tiBar = 6.0, N = 16, T = 5, K=2, PI=3.141593,
Y = structure(
.Data =   c(10.9,10.3,14.7,3.9,8.8,9.3,13.7,12.5,9.1,NA,9.5,22.7,17.2,11.1,8.5,8.9,
6.8,6.1,18.2,3.1,4.4,13.9,5.1,4.8,3.8,11.2,5.1,16.6,4.2,6.8,3.0,5.1,
8.2,6.6,16.3,3.1,8.4,13.5,0.8,7.8,NA,4.1,3.8,17.3,1.6,3.3,3.6,4.8,
8.2,5.3,11.1,2.4,4.5,12.8,4.6,7.0,2.9,5.5,8.6,15.6,1.0,4.7,5.3,4.4,
6.1,5.3,11.9,5.4,6.4,6.9,3.6,5.9,1.7,10.9,5.4,12.2,4.8,6.3,3.6,5.8,
12.8,9.3,13.0,6.0,8.4,8.8,16.7,13.7,8.3,12.8,8.9,21.8,13.3,10.0,6.9,6.7,
8.1,7.0,17.2,1.7,4.4,11.5,5.7,2.0,3.0,10.9,5.3,16.3,1.8,8.3,5.3,4.0,
9.0,7.5,16.4,5.2,7.4,12.3,NA,7.2,5.0,2.9,5.0,16.2,1.2,5.0,4.0,6.6,
11.1,6.4,11.3,2.6,5.7,11.1,3.9,8.1,3.7,5.0,6.2,12.8,0.0,5.2,3.7,4.0,
7.1,7.4,12.1,5.1,6.1,6.2,2.6,6.8,2.8,9.9,NA,11.1,1.7,4.8,3.1,2.7),
.Dim = c(16,5,2)))

m <- jags.model("/Users/dbmad/Documents/G6102/jags/monkey1.bug", 
                data=monkey.data,
		inits=list(monkey.inits1),
                nchain=1
               )
m$update(1000)
parameters <- c("Y","p.inv")
x <- jags.samples(m,parameters,n.iter=10000)
foo<-summary(x$p.inv,mean)$stat
pML <- sum(log(foo))
cat("pML",pML,"\n");

#samples <- coda.samples(m, parameters, 2000)
#summary(samples)

# coda.samples            Generate posterior samples in mcmc.list format
# jags.model              Create a JAGS model object
# jags.module             Dynamically load JAGS modules
# jags.samples            Generate posterior samples
# print.mcarray           Objects for representing MCMC output
# update.jags             Functions for manipulating jags model objects

# as.mcmc.list
# coef(m)