From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press

(c) 2017,  Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida

you are kindly asked to include the complete citation if you used this material in a publication

Code 4.1 Normal linear model in R using JAGS

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require(R2jags)

set.seed(1056)                                    # set seed to replicate example
nobs = 5000                                        # number of observations in model

x1 <- runif(nobs)                                # random uniform variable
beta0 = 2.0                                         # intercept
beta1 = 3.0                                         # slope or coefficient
xb <- beta0 + beta1 * x1                    # linear predictor, xb
y <- rnorm(nobs, xb, sd=1)                # create y as adjusted random normal variate

# Construct data dictionary
X <- model.matrix(~ 1 + x1)
K <- ncol(X)
model.data <- list(Y = y,                               # Response variable
X = X,                             # Predictors
K = K,                             # Number of predictors including the intercept
N = nobs                         # Sample size
)

# Model set up
NORM <- "model{
# Diffuse normal priors for predictors
for (i in 1:K) { beta[i] ~ dnorm(0, 0.0001) }

# Uniform prior for standard deviation
tau <- pow(sigma, -2)                                           # precision
sigma ~ dunif(0, 100)                                           # standard deviation

# Likelihood function
for (i in 1:N){
Y[i] ~ dnorm(mu[i],tau)
mu[i] <- eta[i]
eta[i] <- inprod(beta[], X[i,])
}
}"

# Initial values
inits <- function () {
list(beta = rnorm(K, 0, 0.01))
}

# Parameters to be displayed
params <- c("beta", "sigma")

# MCMC
normfit <- jags(data = model.data,
inits = inits,
parameters = params,
model = textConnection(NORM),
n.chains = 3,
n.iter = 15000,
n.thin = 1,
n.burnin = 10000)

print(normfit, intervals = c(0.025, 0.975), digits = 2)

# Plot the chains to assess mixing
out <- normfit\$BUGSoutput
MyBUGSChains(out,c(uNames("beta",K),"sigma"))

# Display the histograms
out <- normfit\$BUGSoutput
MyBUGSHist(out,c(uNames("beta",K),"sigma"))

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Output on screen:

Inference for Bugs model at "4", fit using jags,

3 chains, each with 15000 iterations (first 10000 discarded)

n.sims = 15000 iterations saved

mu.vect         sd.vect         2.5%         97.5%          Rhat         n.eff

beta               1.99             0.03          1.94            2.05                1       15000

beta               3.01            0.05           2.91            3.10                1       15000

sigma                1.00             0.01           0.98            1.02                1       15000

deviance   14175.64             2.39    14172.91    14181.84               1       15000

For each parameter, n.eff is a crude measure of effective sample size,

and Rhat is the potential scale reduction factor (at convergence, Rhat=1).

DIC info (using the rule, pD = var(deviance)/2)

pD = 2.9 and DIC = 14178.5

DIC is an estimate of expected predictive error (lower deviance is better).  