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visualize.R
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library(car) # why?
AdjustGraphics <- function() {
par(mfrow=c(1,1), mex=0.6,
oma=c(0,0,0,0),
mar=c(5,5,1,1),
cex=0.95, cex.axis=0.8, cex.lab=1, cex.main=1,
mgp=c(3,1,0),
tcl=-0.75,
las=0)
}
BubblePlot <- function( names, x, y,
x.bubble, y.bubble, x.bubble.sd, y.bubble.sd,
props, collapse=TRUE, log=FALSE) {
data <- cbind(x,y)
if (collapse) {
tmp <- aggregate(rep(1, nrow(data)), by=as.list(data.frame(data)), FUN=sum)
combos <- as.matrix(tmp[, 1:2])
freqs <- tmp[,3]
Ncmb <- nrow(tmp)
plot( combos[,2] ~ combos[,1],
cex=50 * freqs / nrow(data),
pch=20, col="#cccccc",
xlab=names[1],ylab=names[2],
xlim=c(0, max(x.bubble) + 1),
ylim=c(0, max(y.bubble) + 1))
} else {
if (log) {
plot( y ~ x, pch=20, col="#cccccc", log="xy",
xlab=names[1], ylab=names[2],
xlim=quantile(x,c(0.1,0.9),na.rm=TRUE),
ylim=quantile(y,c(0.1,0.9),na.rm=TRUE))
} else {
plot( y ~ x, pch=20, col="#cccccc",
xlab=names[1], ylab=names[2],
xlim=c(0, max(x,na.rm=TRUE) + 1), ylim=c(0, max(y, na.rm=TRUE) + 1))
}
}
for (s in 1:length(x.bubble)) {
if (log) {
break
# ellipse( c(x.bubble[s],y.bubble[s]),
# matrix(c(x.bubble[s]*(x.bubble.sd[s]-1),0,0,y.bubble[s]*(y.bubble.sd[s]-1)),nrow=2),radius=1,log="xy",col="#bbbbff",center.pch=FALSE,lwd=1)
} else {
ellipse(c(x.bubble[s], y.bubble[s]),
matrix(c(x.bubble.sd[s], 0, 0, y.bubble.sd[s]), nrow=2),
radius=1, col="#bbbbff", center.pch=FALSE, lwd=1)
}
}
points(y.bubble ~ x.bubble, cex=50 * props)
points(y.bubble ~ x.bubble, pch=3)
}
LLPlot <- function(results) {
graphics1()
segNums <- c()
LL <- c()
AIC <- c()
BIC <- c()
for (i in 1:length(results)) {
segNums = c(segNums, results[[i]]$Model$Segments$Number)
LLval = results[[i]]$Proc$LL
Nparams = results[[i]]$Model$NumberParams
LL = c(LL, LLval)
AIC = c(AIC, LLval - Nparams)
BIC = c(BIC, LLval - 0.5*Nparams*log(length(results[[i]]$Data$Counts[,1])))
}
plot(LL ~ segNums, type='b', col="blue", xlab="Number of Segments")
# points(AIC~segNums, type='b', col="green")
# points(BIC~segNums, type='b', col="red")
}
SegmentProbabilityPlot = function (results) {
segNumRange <- c()
for (i in 1:length(results)) {
segNumRange = c(segNumRange, results[[i]]$Model$Segments$Number)
}
m <- max(segNumRange)
s <- c()
p <- c()
for (i in 1:length(results)) {
s <- c(s, rep(i, times = m))
p <- c(p, sort(results[[i]]$Model$Segments$Probs, decreasing = TRUE), rep(0, times = m - i))
}
barplot(table(s,s) * 0 + p, horiz = TRUE)
# for (segNum in 1:3)
# {
# lambdas = results[[segNum]]$lambdas
# segProbs = sort(results[[segNum]]$segProbs, decreasing=TRUE)
# left = 0
# y = (round(results[[segNum]]$lambdas))[order(-results[[segNum]]$segProbs),]
# for(i in 1:segNum)
# {
# p = segProbs[i];
# if ( p < 0.1)
# break;
# left = left+p/2
# text(left,segNum,paste(sprintf("%d",y[i,]), collapse=","),col="#ffffff",cex=0.5,pos=4,add=TRUE)
# left = left+p/2
# }
# }
}
# Plot implementation for PoissonEMResultSet
plot.PoissonEMResultSet = function (result) {
AdjustGraphics()
lambdas <- round(result$Model$Segments$Counts$Means);
segProbs <- result$Model$Segments$Probs;
counts <- result$Data$Counts
names <- colnames(counts)
if ("LogPayments.Means" %in% names(result$Data)) {
pMeans <- result$Data$LogPayments.Means
mus <- result$Model.Segments.LogPayments.Means
sigmas <- result$Model.Segments.LogPayments.SDs
}
numCats <- ncol(lambdas);
catNames <- colnames(lambdas)
for (i in 1:(numCats - 1)) {
for (j in (i + 1):numCats) {
BubblePlot(names[c(i,j)], counts[, i], counts[, j],
lambdas[, i], lambdas[, j],
sqrt(lambdas[, i]), sqrt(lambdas[, j]), segProbs)
if ("LogPayments.Means" %in% names(result$Data)) {
#bubble.plot(roundTo(pMeans[,i],log(100)), roundTo(pMeans[,j], log(100)), mus[,i], mus[,j], segProbs, FALSE )
BubblePlot(names[c(i, j)], exp(pMeans[, i]), exp(pMeans[, j]),
exp(mus[, i]), exp(mus[, j]),
exp(sigmas[, i]), exp(sigmas[, j]), segProbs, FALSE, TRUE)
}
}
}
# colPalette = c( rgb(rep(19,20),0:19,rep(0,20),maxColorValue=19),
# rgb(18:0,rep(19,19),rep(0,19),maxColorValue=19) )
# getCol = function (x)
# {
# B = 19^(-1/3)
# return(colPalette[round(40/(1+B^x))])
# }
#
# x = lambdas[,1]
# xPmtMean = result$fitStats$Payments.MarginalByColumn$modelMean[1]
# xPmtSD = result$fitStats$Payments.MarginalByColumn$realSD[1] # change to ModelSD
# xPmt = (result$Model.Payment.Means[,1]-xPmtMean)/xPmtSD
# y = lambdas[,2]
# yPmt = result$mus[,2]/result$fitStats$Payments.MarginalByColumn$modelMean[2]
# yPmtMean = result$fitStats$Payments.MarginalByColumn$modelMean[2]
# yPmtSD = result$fitStats$Payments.MarginalByColumn$realSD[2] # change to ModelSD
# yPmt = (result$Model.Payment.Means[,2]-yPmtMean)/yPmtSD
#
# for(s in 1:length(segProbs))
# {
# draw.sector( center = c(x[s], y[s]),
# start.degree = -135, end.degree = 45,
# rou1 = 5*result$segProbs[s],
# col = getCol(xPmt[s]), border = "black")
# draw.sector( center = c(x[s], y[s]),
# start.degree = 45, end.degree = 225,
# rou1 = 5*result$segProbs[s],
# col = getCol(yPmt[s]), border = "black")
# }
}