inst/examples/Jags-Ymet-Xnom2grp-MrobustHet-Example.R

In variani/dbda: R code from Doing Bayesian Data Analysis (DBDA)

# Example for Jags-Ymet-Xnom2grp-MrobustHet.R 
#------------------------------------------------------------------------------- 
# Optional generic preliminaries:
#graphics.off() # This closes all of R's graphics windows.
#rm(list=ls())  # Careful! This clears all of R's memory!


JagsYmetXnom2grpMrobustHet <- function()
{
  out <- list()
  
  oldClass(out) <- "JagsYmetXnom2grpMrobustHet"
  return(out)
}

mod <- JagsYmetXnom2grpMrobustHet()

#------------------------------------------------------------------------------- 
# Load The data file 

myDataFrame = read.csv(system.file("data", "ShohatOphirKAMH2012dataReduced.csv", package = "dbda"))
#yName="Score"
#xName="Group"
#fileNameRoot = "TwoGroupIQrobustHet-" 
fileNameRoot = NULL
#RopeMuDiff=c(-0.5,0.5) ; RopeSdDiff=c(-0.5,0.5) ; RopeEff=c(-0.1,0.1)

# myDataFrame = read.csv( file="ShohatOphirKAMH2012dataReduced.csv" )
xName="Group"
yName="PreferenceIndex"
#fileNameRoot="ShohatOphirKAMH2012data-PI-"
RopeMuDiff=c(-0.1,0.1) ; RopeSdDiff=c(-0.1,0.1) ; RopeEff=c(-0.1,0.1)

# myDataFrame = read.csv( file="ShohatOphirKAMH2012dataReduced.csv" )
# xName="Group"
# yName="GrandTotal"
# fileNameRoot="ShohatOphirKAMH2012data-GT-"
# RopeMuDiff=c(-0.1,0.1) ; RopeSdDiff=c(-0.1,0.1) ; RopeEff=c(-0.1,0.1)

# myDataFrame = read.csv( file="RatLives.csv" )
# xName="Group"
# yName="DaysLive"
# fileNameRoot = "RatLives-" 
# RopeMuDiff=c(-10,10) ; RopeSdDiff=c(-10,10) ; RopeEff=c(-0.1,0.1)

# myDataFrame = read.csv( file="RatLives.csv" )
# xName="Group"
# myDataFrame = cbind( myDataFrame , DaysLiveSq = myDataFrame$DaysLive^2 )
# yName="DaysLiveSq"
# fileNameRoot = "RatLives-DaySq-" 
# RopeMuDiff=c(-100,100) ; RopeSdDiff=c(-100,100) ; RopeEff=c(-0.1,0.1)

graphFileType = "eps" 

#------------------------------------------------------------------------------- 
# Load the relevant model into R's working memory:
#source("Jags-Ymet-Xnom2grp-MrobustHet.R")
#------------------------------------------------------------------------------- 
# Generate the MCMC chain:
mcmcCoda = genMCMC(mod, datFrm=myDataFrame , yName=yName , xName=xName ,
                    numSavedSteps=50000 , saveName=fileNameRoot )
#------------------------------------------------------------------------------- 
# Display diagnostics of chain, for specified parameters:
parameterNames = varnames(mcmcCoda) # get all parameter names
for ( parName in parameterNames ) {
  diagMCMC( codaObject=mcmcCoda , parName=parName , 
                saveName=fileNameRoot , saveType=graphFileType )
}
#------------------------------------------------------------------------------- 
# Get summary statistics of chain:
summaryInfo = smryMCMC(mod, mcmcCoda , RopeMuDiff=RopeMuDiff , 
                        RopeSdDiff=RopeSdDiff , RopeEff=RopeEff ,
                        saveName=fileNameRoot )
show(summaryInfo)
# Display posterior information:
plotMCMC(mod, mcmcCoda , datFrm=myDataFrame , yName=yName , xName=xName , 
          RopeMuDiff=RopeMuDiff , RopeSdDiff=RopeSdDiff , RopeEff=RopeEff ,
          pairsPlot=TRUE , saveName=fileNameRoot , saveType=graphFileType )
#-------------------------------------------------------------------------------