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

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Code 5.13  Beta model in R using JAGS

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require(R2jags)

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# Data
set.seed(1056)                                                      # set seed to replicate example
nobs<-3000                                                          # number of obs in model
x1 <- runif(nobs)                                                  # random normal variable

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# generate toy data
xb <- 0.3 + 1.5 * x1
exb <- exp(-xb)
p <- exb/(1+exb)
theta <- 15
y <- rbeta(nobs, theta*(1-p), theta*p)

 

# construct data dictionary
X <- model.matrix(~1 + x1)
K <- ncol(X)

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model.data <- list(Y = y,                                     # response variable
                              X = X,                                   # predictor matrix
                              K = K,                                   # number of predictors including the intercept
                              N = nobs                               # sample size
)
 

Beta<-"model{
    # Diffuse normal priors for predictors
    for (i in 1:K) { beta[i] ~ dnorm(0, 0.0001) }
    
    # Gamma prior for precision parameter
    theta ~ dgamma(0.01,0.01)

 

    # Likelihood function
    for(i in 1:N){
        Y[i] ~ dbeta(shape1[i],shape2[i])
        shape1[i] <- theta*pi[i]
        shape2[i] <- theta*(1-pi[i])
        logit(pi[i]) <- eta[i]
        eta[i] <- inprod(beta[],X[i,])
    }
}"

 

# A function to generate initial values for mcmc
inits <- function () { list(beta = rnorm(ncol(X), 0, 0.1)) }

 

# Parameters to be displayed in output
params <- c("beta", "theta")

 

BETAfit<- jags(data = model.data ,
                          inits = inits,
                          parameters = params, 
                          model = textConnection(Beta),
                          n.thin = 1,
                          n.chains = 3,
                          n.burnin = 2500,
                          n.iter = 5000)

 

print(BETAfit, justify="left", digits=2)

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