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##### Plot results
# 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
correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))] # 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)
counts = counts*100/nvics
barplot(counts,
xlab="Size of High-Risk Population",
ylab="Percent of Victims Predicted",
names.arg=paste(as.character(popsizes*100),'%',sep=''),
ylim=c(0,max(counts)*1.1),
col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
beside=TRUE)
legend("topleft", inset=0.05,
c("Demographics", "Cascades", "Combined Model"),
fill=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)))
box(which='plot')
par(new=T)
counts = counts/(100/nvics)
barplot(counts,
ylim=c(0,max(counts)*1.1),
col=c(rgb(0,0,1,0),rgb(1,0,0,0),rgb(0,1,0,0)),
beside=TRUE)
axis(side = 4)
mtext(side = 4, line = 3, "Number of Victims Predicted")
#### Precision-Recall Curve
plot(ecdf(correct_rank1),col='red',lwd=2,xlim=c(1,100))
plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T)
plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T)
legend("bottomright", inset=0.05,
c("Demographics Model", "Cascade Model", "Combined Model"),
fill=c('red','darkblue','darkgreen'))
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