diff options
Diffstat (limited to 'R Scripts/predict-victims-plots.R')
| -rw-r--r-- | R Scripts/predict-victims-plots.R | 35 |
1 files changed, 17 insertions, 18 deletions
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R index 0655d28..dee3cdc 100644 --- a/R Scripts/predict-victims-plots.R +++ b/R Scripts/predict-victims-plots.R @@ -1,27 +1,32 @@ ##### 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 -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))), +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=c('0.1%','0.5%','1%'),ylim=c(0,max(counts)*1.1), + 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 (Logit)", "Cascades (Sum parent)", "Combined Model"), + 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') @@ -34,18 +39,12 @@ barplot(counts, 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_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')) -lines(c(0,vcount(lcc)),c(0,1)) |