diff options
Diffstat (limited to 'R Scripts/predict-victims-plots.R')
| -rw-r--r-- | R Scripts/predict-victims-plots.R | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R index b872201..87b0a25 100644 --- a/R Scripts/predict-victims-plots.R +++ b/R Scripts/predict-victims-plots.R @@ -15,14 +15,14 @@ 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)*0.001)), - sum(correct_rank1<(vcount(lcc)*0.005)), +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)*0.005)), 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)*0.005)), sum(correct_rank3<(vcount(lcc)*0.01))), nrow=3, byrow=T) counts = counts*100/nvics @@ -53,7 +53,7 @@ lines(popsizes,counts[3,]) lines(c(0,1),c(0,1)) #### Precision-Recall Curve -plot(ecdf(correct_rank1),col='red',xlim=c(0,vcount(lcc)),lwd=2) +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, |