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Stan-Ymet-Xnom2fac-MnormalHom.R
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Stan-Ymet-Xnom2fac-MnormalHom.R
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# 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.R")
#===============================================================================
genMCMC = function( datFrm , yName="y" , x1Name="x1" , x2Name="x2" ,
numSavedSteps=50000 , thinSteps=1 , saveName=NULL ,
runjagsMethod=runjagsMethodDefault ,
nChains=nChainsDefault ) {
#------------------------------------------------------------------------------
# THE DATA.
# Convert data file columns to generic x,y variable names for model:
y = as.numeric(datFrm[,yName])
x1 = as.numeric(as.factor(datFrm[,x1Name]))
x1levels = levels(as.factor(datFrm[,x1Name]))
x2 = as.numeric(as.factor(datFrm[,x2Name]))
x2levels = levels(as.factor(datFrm[,x2Name]))
Ntotal = length(y)
Nx1Lvl = length(unique(x1))
Nx2Lvl = length(unique(x2))
# Compute scale properties of data, for passing into prior to make the prior
# vague on the scale of the data.
# For prior on baseline, etc.:
yMean = mean(y)
ySD = sd(y)
# For hyper-prior on deflections:
agammaShRa = unlist( gammaShRaFromModeSD( mode=sd(y)/2 , sd=2*sd(y) ) )
# Specify the data in a list for sending to JAGS:
dataList = list(
y = y ,
x1 = x1 ,
x2 = x2 ,
n_total = Ntotal ,
n_x1_lvl = Nx1Lvl ,
n_x2_lvl = Nx2Lvl ,
# data properties for scaling the prior:
a_gamma_sh_ra = agammaShRa
)
#------------------------------------------------------------------------------
# INTIALIZE THE CHAINS.
# Let Stan do it automatically...
#------------------------------------------------------------------------------
# RUN THE CHAINS
require(rstan)
parameters = c( "b0" , "b1" , "b2" , "b1b2" , "m" , "y_sigma" ,
"a1_sd" , "a2_sd" , "a1a2_sd" )
adaptSteps = 1000
burnInSteps = 2000
# Translate to C++ and compile to DSO:
stanDso <- stan_model( file="Ymet-Xnom2fac-MnormalHom.stan" )
# Get MC sample of posterior:
stanFit <- sampling( object=stanDso ,
data = dataList ,
#pars = parameters , # optional
chains = nChains ,
iter = ( ceiling(numSavedSteps/nChains)*thinSteps
+burnInSteps ) ,
warmup = burnInSteps ,
#init = initsList , # optional
)
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 )
}
#===============================================================================
smryMCMC = function( codaSamples ,
datFrm=NULL , x1Name=NULL , x2Name=NULL ,
x1contrasts=NULL , x2contrasts=NULL , x1x2contrasts=NULL ,
saveName=NULL ) {
# All single parameters:
parameterNames = varnames(codaSamples)
if ( !is.null(datFrm) & !is.null(x1Name) & !is.null(x2Name) ) {
x1levels = levels(as.factor(datFrm[,x1Name]))
x2levels = levels(as.factor(datFrm[,x2Name]))
}
summaryInfo = NULL
mcmcMat = as.matrix(codaSamples,chains=TRUE)
for ( parName in parameterNames ) {
summaryInfo = rbind( summaryInfo , summarizePost( mcmcMat[,parName] ) )
thisRowName = parName
if ( !is.null(datFrm) & !is.null(x1Name) & !is.null(x2Name) ) {
# For row name, extract numeric digits from parameter name. E.g., if
# parameter name is "b1b2[12,34]" then pull out b1b2, 12 and 34:
strparts = unlist( strsplit( parName , "\\[|,|\\]" ) )
# if there are only the param name and a single index:
if ( length(strparts)==2 ) {
# if param name refers to factor 1:
if ( substr(strparts[1],nchar(strparts[1]),nchar(strparts[1]))=="1" ) {
thisRowName = paste( thisRowName , x1levels[as.numeric(strparts[2])] )
}
# if param name refers to factor 2:
if ( substr(strparts[1],nchar(strparts[1]),nchar(strparts[1]))=="2" ) {
thisRowName = paste( thisRowName , x2levels[as.numeric(strparts[2])] )
}
}
# if there are the param name and two indices:
if ( length(strparts)==3 ) {
thisRowName = paste( thisRowName , x1levels[as.numeric(strparts[2])],
x2levels[as.numeric(strparts[3])] )
}
}
rownames(summaryInfo)[NROW(summaryInfo)] = thisRowName
}
# All contrasts:
if ( !is.null(x1contrasts) | !is.null(x2contrasts) | !is.null(x1x2contrasts) ) {
if ( is.null(datFrm) | is.null(x1Name) | is.null(x2Name) ) {
show(" *** YOU MUST SPECIFY THE DATA FILE AND FACTOR NAMES TO DO CONTRASTS. ***\n")
} else {
x1 = as.numeric(as.factor(datFrm[,x1Name]))
x1levels = levels(as.factor(datFrm[,x1Name]))
x2 = as.numeric(as.factor(datFrm[,x2Name]))
x2levels = levels(as.factor(datFrm[,x2Name]))
# x1 contrasts:
if ( !is.null(x1contrasts) ) {
for ( cIdx in 1:length(x1contrasts) ) {
thisContrast = x1contrasts[[cIdx]]
left = right = rep(FALSE,length(x1levels))
for ( nIdx in 1:length( thisContrast[[1]] ) ) {
left = left | x1levels==thisContrast[[1]][nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( thisContrast[[2]] ) ) {
right = right | x1levels==thisContrast[[2]][nIdx]
}
right = normalize(right)
contrastCoef = matrix( left-right , ncol=1 )
postContrast = ( mcmcMat[,paste("b1[",1:length(x1levels),"]",sep="")]
%*% contrastCoef )
summaryInfo = rbind( summaryInfo ,
summarizePost( postContrast ,
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE ) )
rownames(summaryInfo)[NROW(summaryInfo)] = (
paste( paste(thisContrast[[1]],collapse=""), ".v.",
paste(thisContrast[[2]],collapse=""),sep="") )
}
}
# x2 contrasts:
if ( !is.null(x2contrasts) ) {
for ( cIdx in 1:length(x2contrasts) ) {
thisContrast = x2contrasts[[cIdx]]
left = right = rep(FALSE,length(x2levels))
for ( nIdx in 1:length( thisContrast[[1]] ) ) {
left = left | x2levels==thisContrast[[1]][nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( thisContrast[[2]] ) ) {
right = right | x2levels==thisContrast[[2]][nIdx]
}
right = normalize(right)
contrastCoef = matrix( left-right , ncol=1 )
postContrast = ( mcmcMat[,paste("b2[",1:length(x2levels),"]",sep="")]
%*% contrastCoef )
summaryInfo = rbind( summaryInfo ,
summarizePost( postContrast ,
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE ) )
rownames(summaryInfo)[NROW(summaryInfo)] = (
paste( paste(thisContrast[[1]],collapse=""), ".v.",
paste(thisContrast[[2]],collapse=""),sep="") )
}
}
# interaction contrasts:
if ( !is.null(x1x2contrasts) ) {
for ( cIdx in 1:length(x1x2contrasts) ) {
thisContrast = x1x2contrasts[[cIdx]]
# factor A contrast:
facAcontrast = thisContrast[[1]]
facAcontrastLeft = facAcontrast[[1]]
facAcontrastRight = facAcontrast[[2]]
left = right = rep(FALSE,length(x1levels))
for ( nIdx in 1:length( facAcontrastLeft ) ) {
left = left | x1levels==facAcontrastLeft[nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( facAcontrastRight ) ) {
right = right | x1levels==facAcontrastRight[nIdx]
}
right = normalize(right)
facAcontrastCoef = left-right
# factor B contrast:
facBcontrast = thisContrast[[2]]
facBcontrastLeft = facBcontrast[[1]]
facBcontrastRight = facBcontrast[[2]]
left = right = rep(FALSE,length(x2levels))
for ( nIdx in 1:length( facBcontrastLeft ) ) {
left = left | x2levels==facBcontrastLeft[nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( facBcontrastRight ) ) {
right = right | x2levels==facBcontrastRight[nIdx]
}
right = normalize(right)
facBcontrastCoef = left-right
# interaction contrast:
contrastCoef = cbind( as.vector(
outer( facAcontrastCoef , facBcontrastCoef ) ) )
postContrast = ( mcmcMat[,substr(colnames(mcmcMat),1,5)=="b1b2["]
%*% contrastCoef )
summaryInfo = rbind( summaryInfo ,
summarizePost( postContrast ,
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE ) )
rownames(summaryInfo)[NROW(summaryInfo)] = (
paste( paste( paste(thisContrast[[1]][[1]],collapse=""),
".v.",
paste(thisContrast[[1]][[2]],collapse=""),sep=""),
".x.",
paste( paste(thisContrast[[2]][[1]],collapse=""),
".v.",
paste(thisContrast[[2]][[2]],collapse=""),sep=""),
sep="") )
}
}
}
}
# Save results:
if ( !is.null(saveName) ) {
write.csv( summaryInfo , file=paste(saveName,"SummaryInfo.csv",sep="") )
}
return( summaryInfo )
}
#===============================================================================
plotMCMC = function( codaSamples ,
datFrm , yName="y" , x1Name="x1" , x2Name="x2" ,
x1contrasts=NULL ,
x2contrasts=NULL ,
x1x2contrasts=NULL ,
saveName=NULL , saveType="jpg" ) {
mcmcMat = as.matrix(codaSamples,chains=TRUE)
chainLength = NROW( mcmcMat )
y = datFrm[,yName]
x1 = as.numeric(as.factor(datFrm[,x1Name]))
x1levels = levels(as.factor(datFrm[,x1Name]))
x2 = as.numeric(as.factor(datFrm[,x2Name]))
x2levels = levels(as.factor(datFrm[,x2Name]))
# Display data with posterior predictive distributions
for ( x2idx in 1:length(x2levels) ) {
openGraph(width=1.2*length(x1levels),height=5)
par( mar=c(4,4,2,1) , mgp=c(3,1,0) )
plot(-10,-10,
xlim=c(0.2,length(x1levels)+0.1) ,
xlab=paste(x1Name,x2Name,sep="\n") ,
xaxt="n" , ylab=yName ,
ylim=c(min(y)-0.2*(max(y)-min(y)),max(y)+0.2*(max(y)-min(y))) ,
main="Data with Post. Pred.")
axis( 1 , at=1:length(x1levels) , tick=FALSE ,
lab=paste( x1levels , x2levels[x2idx] , sep="\n" ) )
for ( x1idx in 1:length(x1levels) ) {
xPlotVal = x1idx #+ (x2idx-1)*length(x1levels)
yVals = y[ x1==x1idx & x2==x2idx ]
points( rep(xPlotVal,length(yVals))+runif(length(yVals),-0.05,0.05) ,
yVals , pch=1 , cex=1.5 , col="red" )
chainSub = round(seq(1,chainLength,length=20))
for ( chnIdx in chainSub ) {
m = mcmcMat[chnIdx,paste("m[",x1idx,",",x2idx,"]",sep="")]
s = mcmcMat[chnIdx,"y_sigma"]
normlim = qnorm( c(0.025,0.975) )
yl = m+normlim[1]*s
yh = m+normlim[2]*s
ycomb=seq(yl,yh,length=201)
ynorm = dnorm(ycomb,mean=m,sd=s)
ynorm = 0.67*ynorm/max(ynorm)
lines( xPlotVal-ynorm , ycomb , col="skyblue" )
}
}
if ( !is.null(saveName) ) {
saveGraph( file=paste0(saveName,"PostPred-",x2levels[x2idx]), type=saveType)
}
} # end for x2idx
# plot contrasts:
if ( !is.null(x1contrasts) | !is.null(x2contrasts) | !is.null(x1x2contrasts) ) {
if ( is.null(datFrm) | is.null(x1Name) | is.null(x2Name) ) {
show(" *** YOU MUST SPECIFY THE DATA FILE AND FACTOR NAMES TO DO CONTRASTS. ***\n")
} else {
x1 = as.numeric(as.factor(datFrm[,x1Name]))
x1levels = levels(as.factor(datFrm[,x1Name]))
x2 = as.numeric(as.factor(datFrm[,x2Name]))
x2levels = levels(as.factor(datFrm[,x2Name]))
# x1 contrasts:
if ( !is.null(x1contrasts) ) {
for ( cIdx in 1:length(x1contrasts) ) {
thisContrast = x1contrasts[[cIdx]]
left = right = rep(FALSE,length(x1levels))
for ( nIdx in 1:length( thisContrast[[1]] ) ) {
left = left | x1levels==thisContrast[[1]][nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( thisContrast[[2]] ) ) {
right = right | x1levels==thisContrast[[2]][nIdx]
}
right = normalize(right)
contrastCoef = matrix( left-right , ncol=1 )
postContrast = ( mcmcMat[,paste("b1[",1:length(x1levels),"]",sep="")]
%*% contrastCoef )
openGraph(height=4,width=4)
compInfo = plotPost( postContrast , xlab="Difference" ,
main=paste0(
paste(thisContrast[[1]],collapse="."),
"\nvs\n",
paste(thisContrast[[2]],collapse=".") ),
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE )
saveGraph(file=paste0(fileNameRoot,
paste(thisContrast[[1]],collapse="."),
"-vs-",
paste(thisContrast[[2]],collapse=".")),
type=graphFileType)
}
}
# x2 contrasts:
if ( !is.null(x2contrasts) ) {
for ( cIdx in 1:length(x2contrasts) ) {
thisContrast = x2contrasts[[cIdx]]
left = right = rep(FALSE,length(x2levels))
for ( nIdx in 1:length( thisContrast[[1]] ) ) {
left = left | x2levels==thisContrast[[1]][nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( thisContrast[[2]] ) ) {
right = right | x2levels==thisContrast[[2]][nIdx]
}
right = normalize(right)
contrastCoef = matrix( left-right , ncol=1 )
postContrast = ( mcmcMat[,paste("b2[",1:length(x2levels),"]",sep="")]
%*% contrastCoef )
openGraph(height=4,width=4)
compInfo = plotPost( postContrast , xlab="Difference" ,
main=paste0(
paste(thisContrast[[1]],collapse="."),
"\nvs\n",
paste(thisContrast[[2]],collapse=".") ),
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE )
saveGraph(file=paste0(fileNameRoot,
paste(thisContrast[[1]],collapse="."),
"-vs-",
paste(thisContrast[[2]],collapse=".")),
type=graphFileType)
}
}
# interaction contrasts:
if ( !is.null(x1x2contrasts) ) {
for ( cIdx in 1:length(x1x2contrasts) ) {
thisContrast = x1x2contrasts[[cIdx]]
# factor A contrast:
facAcontrast = thisContrast[[1]]
facAcontrastLeft = facAcontrast[[1]]
facAcontrastRight = facAcontrast[[2]]
left = right = rep(FALSE,length(x1levels))
for ( nIdx in 1:length( facAcontrastLeft ) ) {
left = left | x1levels==facAcontrastLeft[nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( facAcontrastRight ) ) {
right = right | x1levels==facAcontrastRight[nIdx]
}
right = normalize(right)
facAcontrastCoef = left-right
# factor B contrast:
facBcontrast = thisContrast[[2]]
facBcontrastLeft = facBcontrast[[1]]
facBcontrastRight = facBcontrast[[2]]
left = right = rep(FALSE,length(x2levels))
for ( nIdx in 1:length( facBcontrastLeft ) ) {
left = left | x2levels==facBcontrastLeft[nIdx]
}
left = normalize(left)
for ( nIdx in 1:length( facBcontrastRight ) ) {
right = right | x2levels==facBcontrastRight[nIdx]
}
right = normalize(right)
facBcontrastCoef = left-right
# interaction contrast:
contrastCoef = cbind( as.vector(
outer( facAcontrastCoef , facBcontrastCoef ) ) )
postContrast = ( mcmcMat[,substr(colnames(mcmcMat),1,5)=="b1b2["]
%*% contrastCoef )
mainName = paste0(
paste0(paste(thisContrast[[1]][[1]],collapse="."),
".v.",
paste(thisContrast[[1]][[2]],collapse=".")),
"\n(x)\n",
paste0(paste(thisContrast[[2]][[1]],collapse="."),
".v.",
paste(thisContrast[[2]][[2]],collapse=".")))
openGraph(height=4,width=4)
compInfo = plotPost( postContrast , xlab="Difference of Differences" ,
main=mainName ,
compVal=thisContrast$compVal ,
ROPE=thisContrast$ROPE )
fileNameSuffix = paste0(
paste0(paste(thisContrast[[1]][[1]],collapse="."),
".v.",
paste(thisContrast[[1]][[2]],collapse=".")),
"(x)",
paste0(paste(thisContrast[[2]][[1]],collapse="."),
".v.",
paste(thisContrast[[2]][[2]],collapse=".")))
saveGraph(file=paste0(fileNameRoot,fileNameSuffix),
type=graphFileType)
}
} # end of interaction contrasts
}
} # end plot contrasts
}
#
# #==============================================================================
# # Do NHST ANOVA:
#
# theData = data.frame( y=y , x1=factor(x1,labels=x1levels) ,
# x2=factor(x2,labels=x2levels) )
# openGraph(width=7,height=7)
# interaction.plot( theData$x1 , theData$x2 , theData$y , type="b" )
# #saveGraph( file=paste(fileNameRoot,"DataPlot",sep="") , type="eps" )
# aovresult = aov( y ~ x1 * x2 , data = theData )
# cat("\n------------------------------------------------------------------\n\n")
# print( summary( aovresult ) )
# cat("\n------------------------------------------------------------------\n\n")
# print( model.tables( aovresult , type = "effects", se = TRUE ) , digits=3 )
# cat("\n------------------------------------------------------------------\n\n")
#
# #==============================================================================