plot.sensitivity <- function(){
source('Libraries/GLM.functions.R')
param <- 'hc'
small.thresh <- 1000000 # 100 ha
large.thresh <- 10000000 # 10000 ha
# png(filename = "../Figure_sensitivity.png",
# width = 3, height = 2, units = "in", res=300)
sens.file <- paste("../supporting files/sensitivity_relative_",param,'.tsv',sep='')
sens.table <- read.delim(sens.file,sep='\t')
WBICs <- as.character(sens.table$WBICs)
sens.vals <- sens.table[,-1]
sizes <- vector(length=length(WBICs))
# do size categories too!
for (j in 1:length(WBICs)){
sizes[j] <- getArea(WBICs[j])
}
num.sims <- length(names(sens.vals))
sens.x <- vector(length=num.sims)
head.length <- nchar(paste(param,"_relative_",sep=''))
paste(param,"_relative_",sep='')
for (k in 1:num.sims){
sens.x[k] <- as.numeric(substring(names(sens.vals)[k],head.length+1))
}
comp.idx <- which(sens.x==1)
diffs <- data.frame("small"=vector(length=length(sens.x)),
"medium"=vector(length=length(sens.x)),
"large"=vector(length=length(sens.x)))
small.i <- (sizes<=small.thresh)
med.i <- (sizes>small.thresh & sizes<large.thresh)
large.i <- (sizes>=large.thresh)
for (k in 1:length(sens.x)){
sim.norm <- sens.vals[,comp.idx]
sim.comp <- sens.vals[,k]
diffs$small[k] <- median(sim.comp[small.i]-sim.norm[small.i],na.rm=TRUE)
diffs$medium[k] <- median(sim.comp[med.i]-sim.norm[med.i],na.rm=TRUE)
diffs$large[k] <- median(sim.comp[large.i]-sim.norm[large.i],na.rm=TRUE)
}
plot(sens.x,diffs$small,col='blue',type='l',xlim=c(.4,1.6),ylim=c(-4,4))
points(sens.x,diffs$medium,col='green',type='l')
points(sens.x,diffs$large,col='red',type='l')
}
plot.sensitivity()
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