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##### Plot results
library(RColorBrewer)
library(igraph)
setwd('~/Documents/Violence Cascades/')
load('Raw Data/lcc.RData')
load('Results/correct_rank_91415.RData')
#######
nvics = dim(correct_rank)[1]
correct_rank1 = correct_rank[,length(lambdas)] # demographics model
correct_rank2 = correct_rank[,1] # cascade model
lambda_opt = which.max(colMeans(correct_rank<1382))
correct_rank3 = correct_rank[,lambda_opt] # best combined model
popsizes = c(0.1,0.5,1.0)/100
vcount(lcc)*popsizes
counts = matrix(c( sum(correct_rank1<(vcount(lcc)*popsizes[1])),
sum(correct_rank1<(vcount(lcc)*popsizes[2])),
sum(correct_rank1<(vcount(lcc)*popsizes[3])),
sum(correct_rank2<(vcount(lcc)*popsizes[1])),
sum(correct_rank2<(vcount(lcc)*popsizes[2])),
sum(correct_rank2<(vcount(lcc)*popsizes[3])),
sum(correct_rank3<(vcount(lcc)*popsizes[1])),
sum(correct_rank3<(vcount(lcc)*popsizes[2])),
sum(correct_rank3<(vcount(lcc)*popsizes[3]))),
nrow=3, byrow=T)
cols = brewer.pal(3,'Paired'); cols=cols[c(3,1,2)]
barplot(counts, border=NA,
xlab="Size of High-Risk Population",
ylab="Victims Predicted",
names.arg=paste(as.character(popsizes*100),'%',sep=''),
ylim=c(0,max(counts)*1.1),axes=F,
col=cols,
beside=TRUE)
axis(2, at=pretty(counts*100/nvics)*nvics/100, lab=paste0(pretty(counts*100/nvics), "%"), las=TRUE)
legend("topleft", inset=0.05,
c("Demographics Model", "Contagion Model", "Combined Model"),
fill=cols,border=NA,bty='n')
box(which='plot')
par(new=T)
axis(side=4, at=pretty(counts), lab=pretty(counts))
mtext(side = 4, line = 3, "Number of Victims Predicted")
#### Precision-Recall Curve
plot(ecdf(correct_rank1),col=cols[1],lwd=2,main='',
xlab='Ranking',ylab='CDF',xlim=c(0,140000))
plot(ecdf(correct_rank2),col=cols[2],lwd=2,add=T)
plot(ecdf(correct_rank3),col=cols[3],lwd=2,add=T)
legend("bottomright", inset=0.05,
c("Demographics Model", "Contagion Model", "Combined Model"),
fill=cols,border=NA,bty='n')
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