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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# Data from code 5.20

set.seed(135)
nobs <- 1:2000

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x1 <- runif(nobs)
x2 <- 2*runif(nobs)

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xb <- 2 + 0.75 * x1 - 1.25 * x2

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exb <- pnorm(xb)                                                # probit inverse link

py <- rbinom(nobs, size=1, prob=exb)

probdata <- data.frame(py, x1, x2)

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Code 5.22 Probit model in R using JAGS

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attach(probdata)
require(R2jags)

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set.seed(1944)
X <- model.matrix(~ x1 + x2,
                               data = probdata)

K <- ncol(X)

model.data <- list(Y = probdata$py,
                              N = nrow(probdata),
                              X =X,
                              K =K,
                              LogN = log(nrow(probdata)),
                              b0 = rep(0, K),
                              B0 = diag(0.00001, K)
)
sink("PROBIT.txt")

cat("
model{
    # Priors
    beta ~ dmnorm(b0[], B0[,])

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    # Likelihood
    for (i in 1:N){
        Y[i] ~ dbern(p[i])
        probit(p[i]) <- max(-20, min(20, eta[i]))
        eta[i] <- inprod(beta[], X[i,])
        LLi[i] <- Y[i] * log(p[i]) +
        (1 - Y[i]) * log(1 - p[i])
    }

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    LogL <- sum(LLi[1:N])

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    # Information criteria
    AIC <- -2 * LogL + 2 * K
    BIC <- -2 * LogL + LogN * K
    }", fill = TRUE)

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sink()

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# Initial parameter values
inits <- function () {list(beta = rnorm(K, 0, 0.1)) }

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# parameters and statistics to display
params <- c("beta", "LogL", "AIC", "BIC")

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PROBT <- jags(data = model.data,
                           inits = inits,
                           parameters = params,
                           model.file = "PROBIT.txt",
                           n.thin = 1,
                           n.chains = 3,
                           n.burnin = 5000,
                           n.iter = 10000)

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print(PROBT, intervals=c(0.025, 0.975), digits=3)

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