jagsUI (version 1.5.0)

pp.check: Posterior Predictive Checks for Bayesian Analyses fit in JAGS

Description

A simple interface for generating a posterior predictive check plot for a JAGS analysis fit using jagsUI, based on the posterior distributions of discrepency metrics specified by the user and calculated and returned by JAGS (for example, sums of residuals). The user supplies the name of the discrepancy metric calculated for the real data in the argument actual, and the corresponding discrepancy for data simulated by the model in argument new. The posterior distributions of the two parameters will be plotted in X-Y space and a Bayesian p-value calculated.

Usage

pp.check(x, actual, new)

Arguments

x

A jagsUI object generated using the jags function

actual

The name of the parameter (as a string, in the JAGS model) representing the fit of the actual dataset (e.g. residuals)

new

The name of the corresponding parameter (as a string, in the JAGS model) representing the fit of the new 'ideal' dataset

Examples

# NOT RUN {
#Analyze Longley economic data in JAGS
#Number employed as a function of GNP
#See ?jags for a more detailed example

#Get data
data(longley)
gnp <- longley$GNP
employed <- longley$Employed
n <- length(employed)
data <- list(gnp=gnp,employed=employed,n=n)

#Read in BUGS model file
#Note calculation of discrepancy stats fit and fit.new
#(sums of residuals)
writeLines("
model{

  #Likelihood
  for (i in 1:n){ 

    employed[i] ~ dnorm(mu[i], tau)     
    mu[i] <- alpha + beta*gnp[i]
    
    res[i] <- employed[i] - mu[i]   
    emp.new[i] ~ dnorm(mu[i], tau)
    res.new[i] <- emp.new[i] - mu[i]

  }
    
  #Priors
  alpha ~ dnorm(0, 0.00001)
  beta ~ dnorm(0, 0.00001)
  sigma ~ dunif(0,1000)
  tau <- pow(sigma,-2)
  
  #Derived parameters
  fit <- sum(res[])
  fit.new <- sum(res.new[])

}
", con="model.txt")

#Identify filepath of model file;
#in this case in the working directory
modfile <- 'model.txt'
  
#Set parameters to monitor
params <- c('alpha','beta','sigma','fit','fit.new')

#Run analysis

out <- jags(data = data,
            inits = NULL,
            parameters.to.save = params,
            model.file = modfile,
            n.chains = 3,
            n.adapt = 100,
            n.iter = 1000,
            n.burnin = 500,
            n.thin = 2)

#Examine output summary

out

#Posterior predictive check plot

pp.check(out, actual = 'fit', new = 'fit.new')

# }

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