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cbd
2007 Feb 27
0
Optimizing the loop for large data
...1:1008 -86.27151 39.99040 4.589226 1.2212674 49850.92
4440021 40 0 0
10 TAZ 18011:9215 -86.58085 40.13588 37.278521 69.6681954 24438.13
4457794 2095 85 200
thrs<-seq(1000,10000,length=50)
ED<-D[,383]/D[,5] # employment density
CBDx<-D[,6]-58277.194363 # convert a coordinate for x
CBDy<-D[,7]-4414486.03135 # convert a coordinate for y
AER<-vector("numeric",length(thrs))
OER<-vector("numeric",length(thrs))
MER<-vector("numeric",length(thrs))
# compute the apparent error rates for each threshold value
for (j in 1:length(thrs)){
ctgy<-i...