MultiSpeciesOcc.knownN = function() {

  Ymat = as.matrix(read.csv('detectionFreq.NH17.csv'))
  #Ymat = as.matrix(read.csv('detectionFreq.AL17.csv'))
  nrepls = 11

  start.time = Sys.time()
  
  n = dim(Ymat)[1]
  nsites = dim(Ymat)[2]

  # create arguments for bugs()
  data = list(n=n, R=nsites, J=nrepls, Y=Ymat)

  params = list('alpha', 'beta', 'rho', 'sigma.u', 'sigma.v','phi','eta')

  inits = function() {
    psi.meanGuess = runif(1, .25,1)
    p.meanGuess = runif(1, .25,1)
    rhoGuess = runif(1, 0,1)
    sigma.uGuess = runif(1,0,1.5)
    sigma.vGuess = runif(1,0,1.5)
    list(psi.mean=psi.meanGuess, p.mean=p.meanGuess,
         sigma.u=sigma.uGuess, sigma.v=sigma.vGuess, rho=rhoGuess,
         phi=rnorm(n, log(psi.meanGuess/(1-psi.meanGuess)), sigma.uGuess),
         eta=rnorm(n, log(p.meanGuess/(1-p.meanGuess)), sigma.vGuess),
         Z = matrix(rbinom(n*nsites, size=1, prob=psi.meanGuess), nrow=n)
       )
  }

  
  modelFilename = "MultiSpeciesOccModelKnownN.txt"

  cat("
model {

psi.mean ~ dunif(0,1)
beta <- log(psi.mean) - log(1-psi.mean)

p.mean ~ dunif(0,1)
alpha <- log(p.mean) - log(1-p.mean)

sigma.u ~ dunif(0,10)
sigma.v ~ dunif(0,10)
tau.u <- pow(sigma.u,-2)
tau.v <- pow(sigma.v,-2)

rho ~ dunif(-1,1)
var.eta <- tau.v/(1.-pow(rho,2))



for (i in 1:n) {
    phi[i] ~ dnorm(beta, tau.u)

    mu.eta[i] <- alpha + (rho*sigma.v/sigma.u)*(phi[i] - beta)
    eta[i] ~ dnorm(mu.eta[i], var.eta)


    logit(psi[i]) <- phi[i]
    logit(p[i]) <- eta[i]

    for (k in 1:R) {
	Z[i,k] ~ dbern(psi[i])
	mu.p[i,k] <- p[i]*Z[i,k]
	Y[i,k] ~ dbin(mu.p[i,k], J)
    }
}

}
", fill=TRUE, file=modelFilename)
  

  # fit model to data using WinBUGS code
  library(R2WinBUGS)
  fit = bugs(data, inits, params, modelFilename,
    n.chains=5, n.iter=55000, n.burnin=5000, n.thin=50, bugs.seed=sample(1:9999,size=1), debug=FALSE, DIC=FALSE)
  
  end.time = Sys.time()
  elapsed.time = difftime(end.time, start.time, units='mins')
  cat(paste(paste('Posterior computed in ', elapsed.time, sep=''), ' minutes\n', sep=''))
  
  fit
}