R2OpenBUGS-PC-Study-Guide-4a-three-chains-FEV-Regression-Fa16.R

## SDS384.7 Study Guide #4a, Fall 2016
## Pre (before drawing the MCMC samples) processing FEV data in R

# PART 1
## Perform EDA on FEV data set

fevstar.dat  = data.frame(read.table("/Users/chenyinglong/Documents/Bayesian Statistics Methods/FEV-Data-CJBH.txt", header=T))

attach(fevstar.dat)
fevstar.dat[1:3,]

ystar1 = subset(FEV, Smoke == 0)
length(ystar1)

ystar2 = subset(FEV, Smoke == 1)
length(ystar2)


#Side-by-side Box-plots
data.list = list(Nonsmoker = ystar1, Smoker = ystar2)
quartz(width=4,height=4,pointsize=8)
boxplot(data.list, main="Side-by-side Box-plots of FEV Data", ylab="FEV", xlab = "")

# Regression - Scatter Plots
quartz(width=8, height=8,pointsize=8)
plot(fevstar.dat)
title('Matrix of scatter plots - FEV Data')

## check on the propriety of the joint posterior
# An indirect check on the full-rank of the design matrix X in the regression model
fevstar.mat = cbind(rep(1, 345), Age, Smoke, Age*Smoke)
dim(fevstar.mat)

fevstar.mat[1:5,]

xprimex = crossprod(fevstar.mat)
dim(xprimex)

det(xprimex)

## end of EDA
## BDA begins

# PART 2

## Partial Remote processing to get MCMC samples

## Install first the R-package R2OpenBUGS

library(R2OpenBUGS)

setwd("/Users/chenyinglong/Documents/Bayesian Statistics Methods")
getwd()

fev.dat =list(n=nrow(fevstar.dat),Age=Age, FEV=FEV, Smoke = Smoke)

bugs.data(data=fev.dat, data.file="FEV-Regression.txt")

## Now leave R and launch OpenBUGS to load data manually using
# FEV-Regression.txt; Open this text file from OpenBUGS and change file type as .txt

# PART 3

## Full Remote processing to get MCMC samples
## without leaving R

OpenBUGS.pgm="/Users/chenyinglong/.wine/drive_c/Program\ Files/OpenBUGS/OpenBUGS323/OpenBUGS.exe"

set.seed(100)

library(R2OpenBUGS)

setwd("/Users/chenyinglong/Documents/Bayesian Statistics Methods")
getwd()

fev.dat =list(n=nrow(fevstar.dat),Age=Age, FEV=FEV, Smoke = Smoke)

#INITS - Chain 1
inits1 =list(tau=1.0, beta = c(0.0, 0.0, 0.0, 0.0))
#INITS - Chain 2
inits2 =list(tau= 5.0, beta = c(1.0, 2.0, 3.0, 4.0))
#INITS - Chain 3
inits3 =list(tau=10.0, beta = c(0.0, -2.0, -3.0, -4.0))


inits=list(inits1, inits2, inits3)


model.fit.SG4a.with.coda= bugs(data=fev.dat,inits,OpenBUGS.pgm=OpenBUGS.pgm,model.file = "OpenBUGS-txt-study-guide-4a-Fa16.txt",
                               n.burnin=500,parameters=c("FEV20ns","beta[4]"),
                               n.chains = 3,n.iter=5500, codaPkg = TRUE, useWINE = TRUE)

## avoid this print(model.fit.SG4a.with.coda,digits=4),plot(result.SG4a.with.coda)

## Post (after drawing the MCMC samples) processing in R to check for convergence of
## MCMC samples to the posteriors

library(coda)
library(lattice)
out.coda = read.bugs(model.fit.SG4a.with.coda)
class(out.coda)
str(out.coda)

#dev.off()
quartz(width=4,height=4,pointsize=8)
xyplot(out.coda)
quartz(width=4,height=4,pointsize=8)
densityplot(out.coda)


#Brooks-Gelman-Rubin-plot
#Work with samples from the posterior densities of nodes - ONE at a time
beta4.sample.only = bugs(data=fev.dat,inits,OpenBUGS.pgm=OpenBUGS.pgm,model.file = "OpenBUGS-txt-study-guide-4a-Fa16.txt",
                         n.burnin=500,parameters=c("beta[4]"),
                         n.chains = 3,n.iter=5500, codaPkg = TRUE, useWINE = TRUE)
o1 = read.bugs(beta4.sample.only)
quartz(width=4,height=4,pointsize=8)
class(o1)
str(o1)
gelman.plot(o1)

## Assuming that CODA analysis provides no serious concerns of non-convergence
## of MCMC samples to the posteriors we seek, call bugs function again in R2OpenBUGS
## with codaPkg = FALSE to produce node statistics, DIC, etc
## for ALL nodes defined in "parameters"

model.fit.SG4a=bugs(data=fev.dat,inits = inits,OpenBUGS.pgm=OpenBUGS.pgm,model.file = "OpenBUGS-txt-study-guide-4a-Fa16.txt",
                    n.burnin=500,parameters=c("FEV20ns","post.prob1"),
                    n.chains = 3,n.iter=5500, bugs.seed = 12, codaPkg = FALSE, useWINE = TRUE)

print(model.fit.SG4a,digits=4)
quartz(width=4,height=4,pointsize=8)
plot(model.fit.SG4a)