##### Plot results
hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
     xlab='Risk Ranking of Victims',main='')
hist(correct_rank1,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),add=T)
legend("topright", c("Demographics Model", "Cascade Model"), 
       fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))

counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
                  colSums(correct_rank<(vcount(lcc)/200))*100/nvics,
                  colSums(correct_rank<(vcount(lcc)/100))*100/nvics),
                nrow=3, byrow=T)
plot(lambdas,counts[1,],log='x',type='l')

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
counts = matrix(c( sum(correct_rank1<(vcount(lcc)*0.0005)),
                  sum(correct_rank1<(vcount(lcc)*0.001)),
                  sum(correct_rank1<(vcount(lcc)*0.01)),
                  sum(correct_rank2<(vcount(lcc)*0.0005)),
                  sum(correct_rank2<(vcount(lcc)*0.001)),
                  sum(correct_rank2<(vcount(lcc)*0.01)),
                  sum(correct_rank3<(vcount(lcc)*0.0005)),
                  sum(correct_rank3<(vcount(lcc)*0.001)),
                  sum(correct_rank3<(vcount(lcc)*0.01))),
                nrow=3, byrow=T)
counts = counts*100/nvics
barplot(counts, 
        xlab="Size of High-Risk Population",
        ylab="Percent of Victims Predicted",
        names.arg=c('0.1%','0.5%','1%'),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 (Logit)", "Cascades (Sum parent)", "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")

popsizes = c(0.1, 0.5, 1)
plot(popsizes,counts[1,],type='l',ylim=c(0,max(counts)))
lines(popsizes,counts[2,])
lines(popsizes,counts[3,])
lines(c(0,1),c(0,1))

#### Precision-Recall Curve
plot(ecdf(correct_rank1),col='red',xlim=c(0,50),lwd=2)
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'))
lines(c(0,vcount(lcc)),c(0,1))