jags_logit: Fitted JAGS logit model
In BayesPostEst: Generate Postestimation Quantities for Bayesian MCMC Estimation

Description Usage Format Examples

A fitted JAGS logit model generated with [R2jags::jags()]. See the example code below for how it was created. Used in examples and for testing.

1	jags_logit

A class "rjags" object created by [R2jags::jags()]

if (interactive()) {
data("sim_data")
  
## formatting the data for jags
datjags <- as.list(sim_data)
datjags$N <- length(datjags$Y)

## creating jags model
model <- function()  {

  for(i in 1:N){
    Y[i] ~ dbern(p[i])  ## Bernoulli distribution of y_i
      logit(p[i]) <- mu[i]    ## Logit link function
      mu[i] <- b[1] +
        b[2] * X1[i] +
        b[3] * X2[i]
  }

  for(j in 1:3){
    b[j] ~ dnorm(0, 0.001) ## Use a coefficient vector for simplicity
  }

}

params <- c("b")
inits1 <- list("b" = rep(0, 3))
inits2 <- list("b" = rep(0, 3))
inits <- list(inits1, inits2)

## fitting the model with R2jags
set.seed(123)
jags_logit <- R2jags::jags(data = datjags, inits = inits,
                         parameters.to.save = params, n.chains = 2, 
                         n.iter = 2000, n.burnin = 1000, model.file = model)

}