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# Loss aversion based c* calculation routines
# for simulated marginal effects
# based on Esarey and Danneman, "A Quantitative Method for Substantive Robustness Assessment"
# November 24, 2012
#
# Written by Justin Esarey, Rice University
#
#
cstarme<-function(sims, r){
# translates regression results into a "loss point" using a simple kinked loss function
# give it regression results, it returns a corresponding set of "loss points" t
# if the point at which you begin making losses is less than t, accept evidence, else reject
signind<-sign(mean(sims))
if(signind==-1){sims<-(-1)*sims}
utility<-function(x,t,a,sims){
out<-c()
for(i in 1:length(x)){
k<-as.numeric((x[i]-t)>0)*2-1
out[i]<-a^(-k)*density(sims, from=x[i],to=x[i], n=1)$y*(x[i]-t);
}
return(out)
}
maximand<-function(t,a,sims){as.numeric(integrate(utility, min(sims)-sd(sims), max(sims)+sd(sims), t=t, a=a, sims=sims, stop.on.error=FALSE)[1])}
ans<-uniroot(maximand, interval=c(min(sims), max(sims)), a=r, sims=sims)$root
if(signind==-1){ans<-(-1)*ans}
if(signind!=sign(ans)){ans<-0}
return(ans)
}
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