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| author | Ben Green <bgreen@g.harvard.edu> | 2015-09-17 23:37:30 -0400 |
|---|---|---|
| committer | Ben Green <bgreen@g.harvard.edu> | 2015-09-17 23:37:32 -0400 |
| commit | c2eec4bb82cffe6ac454f4de62e828fe176c7e5c (patch) | |
| tree | 4e4d601fb695f8a0d4e30defc35313b12b8c0a77 /R Scripts/predict-victims-plots.R | |
| parent | d943b78f633c5b783ce0311f3861f02502dca263 (diff) | |
| download | criminal_cascades-c2eec4bb82cffe6ac454f4de62e828fe176c7e5c.tar.gz | |
some plotting stuff
Diffstat (limited to 'R Scripts/predict-victims-plots.R')
| -rw-r--r-- | R Scripts/predict-victims-plots.R | 31 |
1 files changed, 17 insertions, 14 deletions
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R index 3dfba60..179a60b 100644 --- a/R Scripts/predict-victims-plots.R +++ b/R Scripts/predict-victims-plots.R @@ -1,5 +1,11 @@ ##### Plot results -# load('Results/correct_rank_91415.RData') +library(RColorBrewer) +library(igraph) +setwd('~/Documents/Violence Cascades/') +load('Raw Data/lcc.RData') +load('Results/correct_rank_91415.RData') + +####### nvics = dim(correct_rank)[1] correct_rank1 = correct_rank[,length(lambdas)] # demographics model @@ -18,33 +24,30 @@ 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) -barplot(counts, +cols = brewer.pal(3,'Paired'); cols=cols[c(3,1,2)] +barplot(counts, border=NA, xlab="Size of High-Risk Population", ylab="Victims Predicted", names.arg=paste(as.character(popsizes*100),'%',sep=''), 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)), + col=cols, 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 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))) + fill=cols,border=NA,bty='n') box(which='plot') - par(new=T) -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,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,main='') -plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T) -plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T) +plot(ecdf(correct_rank1),col=cols[1],lwd=2,main='', + xlab='Ranking',ylab='CDF',xlim=c(0,140000)) +plot(ecdf(correct_rank2),col=cols[2],lwd=2,add=T) +plot(ecdf(correct_rank3),col=cols[3],lwd=2,add=T) legend("bottomright", inset=0.05, - c("Demographics Model", "Cascade Model", "Combined Model"), - fill=c('red','darkblue','darkgreen')) + c("Demographics Model", "Contagion Model", "Combined Model"), + fill=cols,border=NA,bty='n') |