updated prediction plotting scripts

2 files changed, 11 insertions, 11 deletions

diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
index dee3cdc..3dfba60 100644
--- a/R Scripts/predict-victims-plots.R
+++ b/R Scripts/predict-victims-plots.R
@@ -4,7 +4,8 @@
 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
+lambda_opt = which.max(colMeans(correct_rank<1382))
+correct_rank3 = correct_rank[,lambda_opt] # 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])),
@@ -17,31 +18,30 @@ counts = matrix(c( sum(correct_rank1<(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",
+        ylab="Victims Predicted",
         names.arg=paste(as.character(popsizes*100),'%',sep=''),
-        ylim=c(0,max(counts)*1.1),
+        ylim=c(0,max(counts)*1.1),axes=F,
         col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
         beside=TRUE)
+axis(2, at=pretty(counts*100/nvics)*nvics/100, lab=paste0(pretty(counts*100/nvics), "%"), las=TRUE)
 legend("topleft", inset=0.05, 
-       c("Demographics", "Cascades", "Combined Model"), 
+       c("Demographics Model", "Contagion Model", "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)
+        beside=TRUE,axes=F)
+axis(side=4, at=pretty(counts), lab=pretty(counts))
 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_rank1),col='red',lwd=2,main='')
 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, 
diff --git a/R Scripts/predict-victims.R b/R Scripts/predict-victims.R
index 7caaf13..3a531db 100644
--- a/R Scripts/predict-victims.R
+++ b/R Scripts/predict-victims.R
@@ -48,7 +48,7 @@ days = Reduce(union, list(lcc_verts$fatal_day,lcc_verts$nonfatal_day_1,
 days = days[!is.na(days)]
 days = sort(days)
 days = split(days, ceiling(seq_along(days)/92))
-lambdas = c(0, exp(seq(log(0.0000001), log(.95), length.out=100)), 1)
+lambdas = c(0, exp(seq(log(0.0001), log(0.01), length.out=100)), 1)
 
 ##### Loop through days
 correct_rank = c()
@@ -106,4 +106,4 @@ for(i in 1:length(days)){
   print(proc.time()-ptm)
 }
 
-# save(correct_rank, file='Results/correct_rank_91415.RData') 
\ No newline at end of file
+# save(correct_rank, file='Results/correct_rank_91515.RData') 
\ No newline at end of file