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Stan-Ydich-XmetMulti-Mlogistic.R
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Stan-Ydich-XmetMulti-Mlogistic.R
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# Jags-Ydich-XmetMulti-Mlogistic.R
# Accompanies the book:
# Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:
# A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.
# Adapted for Stan by Joe Houpt
source("DBDA2E-utilities-stan.R")
#===========================================================================
genMCMC = function( data , xName="x" , yName="y" ,
numSavedSteps=10000 , thinSteps=1 , saveName=NULL ,
nChains=nChainsDefault ) {
require(rstan)
#-------------------------------------------------------------------------
# THE DATA.
y = data[,yName]
x = as.matrix(data[,xName],ncol=length(xName))
# Do some checking that data make sense:
if ( any( !is.finite(y) ) ) { stop("All y values must be finite.") }
if ( any( !is.finite(x) ) ) { stop("All x values must be finite.") }
cat("\nCORRELATION MATRIX OF PREDICTORS:\n ")
show( round(cor(x),3) )
cat("\n")
flush.console()
# Specify the data in a list, for later shipment to Stan:
dataList = list(
x = x ,
y = y ,
dim_x = dim(x)[2] ,
n_total = dim(x)[1]
)
#-----------------------------------------------------------------------------
# INTIALIZE THE CHAINS.
# Let Stan do it...
#-----------------------------------------------------------------------------
# RUN THE CHAINS
parameters = c( "beta0" , "beta" ,
"zbeta0" , "zbeta" )
adaptSteps = 500 # Number of steps to "tune" the samplers
burnInSteps = 1000
# Translate to C++ and compile to DSO:
stanDso <- stan_model(file="Ydich-XmetMulti-Mlogistic.stan")
stanFit <- sampling( object=stanDso,
pars=parameters ,
data=dataList ,
chains=nChains ,
warmup=burnInSteps ,
iter=(ceiling(numSavedSteps/nChains)*thinSteps+burnInSteps) ,
thin=thinSteps ,
init='random')
codaSamples = mcmc.list( lapply( 1:ncol(stanFit) ,
function(x) { mcmc(as.array(stanFit)[,x,]) } ) )
# resulting codaSamples object has these indices:
# codaSamples[[ chainIdx ]][ stepIdx , paramIdx ]
if ( !is.null(saveName) ) {
save( codaSamples , file=paste(saveName,"Mcmc.Rdata",sep="") )
}
return( codaSamples )
} # end function
#===============================================================================
smryMCMC = function( codaSamples ,
saveName=NULL ) {
summaryInfo = NULL
mcmcMat = as.matrix(codaSamples)
paramName = colnames(mcmcMat)
for ( pName in paramName ) {
summaryInfo = rbind( summaryInfo , summarizePost( mcmcMat[,pName] ) )
}
rownames(summaryInfo) = paramName
if ( !is.null(saveName) ) {
write.csv( summaryInfo , file=paste(saveName,"SummaryInfo.csv",sep="") )
}
return( summaryInfo )
}
#===============================================================================
plotMCMC = function( codaSamples , data , xName="x" , yName="y" ,
showCurve=FALSE , pairsPlot=FALSE ,
saveName=NULL , saveType="jpg" ) {
# showCurve is TRUE or FALSE and indicates whether the posterior should
# be displayed as a histogram (by default) or by an approximate curve.
# pairsPlot is TRUE or FALSE and indicates whether scatterplots of pairs
# of parameters should be displayed.
#-----------------------------------------------------------------------------
y = data[,yName]
x = as.matrix(data[,xName])
mcmcMat = as.matrix(codaSamples,chains=TRUE)
chainLength = NROW( mcmcMat )
zbeta0 = mcmcMat[,"zbeta0"]
zbeta = mcmcMat[,grep("^zbeta$|^zbeta\\[",colnames(mcmcMat))]
if ( ncol(x)==1 ) { zbeta = matrix( zbeta , ncol=1 ) }
beta0 = mcmcMat[,"beta0"]
beta = mcmcMat[,grep("^beta$|^beta\\[",colnames(mcmcMat))]
if ( ncol(x)==1 ) { beta = matrix( beta , ncol=1 ) }
#-----------------------------------------------------------------------------
if ( pairsPlot ) {
# Plot the parameters pairwise, to see correlations:
openGraph()
nPtToPlot = 1000
plotIdx = floor(seq(1,chainLength,by=chainLength/nPtToPlot))
panel.cor = function(x, y, digits=2, prefix="", cex.cor, ...) {
usr = par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r = (cor(x, y))
txt = format(c(r, 0.123456789), digits=digits)[1]
txt = paste(prefix, txt, sep="")
if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex=1.5 ) # was cex=cex.cor*r
}
pairs( cbind( beta0 , beta )[plotIdx,] ,
labels=c( "beta[0]" ,
paste0("beta[",1:ncol(beta),"]\n",xName) ) ,
lower.panel=panel.cor , col="skyblue" )
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"PostPairs",sep=""), type=saveType)
}
}
#-----------------------------------------------------------------------------
# Data with posterior predictive:
# If only 1 predictor:
if ( ncol(x)==1 ) {
openGraph(width=7,height=6)
par( mar=c(3.5,3.5,2,1) , mgp=c(2.0,0.7,0) )
plot( x[,1] , y , xlab=xName[1] , ylab=yName ,
cex=2.0 , cex.lab=1.5 , col="black" , main="Data with Post. Pred." )
abline(h=0.5,lty="dotted")
cVec = floor(seq(1,chainLength,length=30))
xWid=max(x)-min(x)
xComb = seq(min(x)-0.1*xWid,max(x)+0.1*xWid,length=201)
for ( cIdx in cVec ) {
lines( xComb , 1/(1+exp(-(beta0[cIdx]+beta[cIdx,1]*xComb ))) , lwd=1.5 ,
col="skyblue" )
xInt = -beta0[cIdx]/beta[cIdx,1]
arrows( xInt,0.5, xInt,-0.04, length=0.1 , col="skyblue" , lty="dashed" )
}
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"DataThresh",sep=""), type=saveType)
}
}
# If only 2 predictors:
if ( ncol(x)==2 ) {
openGraph(width=7,height=7)
par( mar=c(3.5,3.5,2,1) , mgp=c(2.0,0.7,0) )
plot( x[,1] , x[,2] , pch=as.character(y) , xlab=xName[1] , ylab=xName[2] ,
col="black" , main="Data with Post. Pred.")
cVec = floor(seq(1,chainLength,length=30))
for ( cIdx in cVec ) {
abline( -beta0[cIdx]/beta[cIdx,2] , -beta[cIdx,1]/beta[cIdx,2] , col="skyblue" )
}
if ( !is.null(saveName) ) {
saveGraph( file=paste(saveName,"DataThresh",sep=""), type=saveType)
}
}
#-----------------------------------------------------------------------------
# Marginal histograms:
decideOpenGraph = function( panelCount , saveName , finished=FALSE ,
nRow=1 , nCol=3 ) {
# If finishing a set:
if ( finished==TRUE ) {
if ( !is.null(saveName) ) {
saveGraph( file=paste0(saveName,ceiling((panelCount-1)/(nRow*nCol))),
type=saveType)
}
panelCount = 1 # re-set panelCount
return(panelCount)
} else {
# If this is first panel of a graph:
if ( ( panelCount %% (nRow*nCol) ) == 1 ) {
# If previous graph was open, save previous one:
if ( panelCount>1 & !is.null(saveName) ) {
saveGraph( file=paste0(saveName,(panelCount%/%(nRow*nCol))),
type=saveType)
}
# Open new graph
openGraph(width=nCol*7.0/3,height=nRow*2.0)
layout( matrix( 1:(nRow*nCol) , nrow=nRow, byrow=TRUE ) )
par( mar=c(4,4,2.5,0.5) , mgp=c(2.5,0.7,0) )
}
# Increment and return panel count:
panelCount = panelCount+1
return(panelCount)
}
}
# Original scale:
panelCount = 1
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMarg") )
histInfo = plotPost( beta0 , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(beta[0]) , main="Intercept" )
for ( bIdx in 1:ncol(beta) ) {
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMarg") )
histInfo = plotPost( beta[,bIdx] , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(beta[.(bIdx)]) , main=xName[bIdx] )
}
panelCount = decideOpenGraph( panelCount , finished=TRUE , saveName=paste0(saveName,"PostMarg") )
# Standardized scale:
panelCount = 1
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMargZ") )
histInfo = plotPost( zbeta0 , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(z*beta[0]) , main="Intercept" )
for ( bIdx in 1:ncol(beta) ) {
panelCount = decideOpenGraph( panelCount , saveName=paste0(saveName,"PostMargZ") )
histInfo = plotPost( zbeta[,bIdx] , cex.lab = 1.75 , showCurve=showCurve ,
xlab=bquote(z*beta[.(bIdx)]) , main=xName[bIdx] )
}
panelCount = decideOpenGraph( panelCount , finished=TRUE , saveName=paste0(saveName,"PostMargZ") )
#-----------------------------------------------------------------------------
}
#===============================================================================