Reproduce de Schoot et al. (2014) Bayesian Analysis

gistfile1.txt

## Model from appendix 3
```
model{
 # There are 20 individuals.
 for (i in 1:n) {
 # The dependent variable has a mean (beta) and prior precision (tau).
 y[i] ~ dnorm(beta[i], tau)
 # Beta consists of an intercept: mu (which is, without any predictors in the model equal to the mean of our dependent variable)
 beta[i] <- mu [i]}
 # Mu (=mean of dependent variable) has a normal prior distribution with a mean of 80 and a prior precision of .01 and the prior is limited to obtain scores between 40 and 180. The prior precision of the dependent variable, tau, has a inverse gamma prior distribution.
 mu ~ dnorm(80,.01)
 #(40,180)
 tau ~ dgamma(0.01, 0.01)
}
```

## R script to call WinBUGS
```
# clears workspace:  
rm(list=ls()) 

# sets working directories:
setwd("D:/core/LAB/Analysis/R/Bayes/8steps")

library(R2WinBUGS)
bugsdir <- "D:/Apps/WinBUGS14"

y <- c(106.2451019,
       130.0098114,	
       105.0036545,	
       108.0337601,	
       100.6291046,	
       96.66761017,	
       107.7711563,	
       102.5716476,	
       96.08522034,	
       119.5092163,	
       72.42178345,	
       130.16362,	
       92.96968079,	
       95.4822998,	
       105.3284225,	
       93.37817383,	
       87.68320465,	
       122.9314728,	
       78.78004456,	
       88.33502197)
n <- length(y)

data <- list("y", "n") # to be passed on to WinBUGS
myinits <-	list(
        list(mu = 100, sigma = 100))

# parameters to be monitored:	
parameters <- c("mu", "tau")

samples = bugs(data, inits=myinits, parameters,
               model.file ="deSchoot.txt",
               n.chains=1, n.iter=1000, n.burnin=1, n.thin=1,
               DIC=T, bugs.directory=bugsdir,
               codaPkg=F, debug=T)
```