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-rw-r--r--R Scripts/predict-victims-plots.R9
1 files changed, 5 insertions, 4 deletions
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
index 8a93667..2ac62c8 100644
--- a/R Scripts/predict-victims-plots.R
+++ b/R Scripts/predict-victims-plots.R
@@ -2,7 +2,7 @@
hist(correct_rank3,150,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(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)))
@@ -12,9 +12,9 @@ counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
nrow=3, byrow=T)
plot(lambdas,counts[1,],log='x',type='l')
-correct_rank1 = correct_rank[,length(lambdas)]
-correct_rank2 = correct_rank[,1]
-correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))]
+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)),
sum(correct_rank1<(vcount(lcc)*0.01)),
@@ -59,3 +59,4 @@ 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))
+