cat("\n\nTYPE IN AN INTEGER SEED [1-9999999] FOLLOWED BY \"ENTER\"\n")
test<-scan(file="",what=integer(0),n=1)

seed.val<-test
if(seed.val>0)

{
set.seed(seed.val)
}

cat("\n\nTYPE IN THE PREVALENCE OF EXPOSURE [0.001-0.999] FOLLOWED BY \"ENTER\"\n")
test<-scan(file="",what=double(0),n=1)

prevalence.exp<-test

cat("\n\nTYPE IN THE RELIABILITY OF EXPOSURE [0.001-0.999] FOLLOWED BY \"ENTER\"\n")
test<-scan(file="",what=double(0),n=1)

reliability<-test


sample.size<-4000000

var.b<-reliability
var.w<-(1-var.b)

pheno.orig<-rnorm(sample.size,0,sqrt(var.b))
error.obs1<-rnorm(sample.size,0,sqrt(var.w))
error.obs2<-rnorm(sample.size,0,sqrt(var.w))
pheno.obs1<-pheno.orig+error.obs1
pheno.obs2<-pheno.orig+error.obs2

pheno.true<-pheno.orig




cov.table<-cov(cbind(pheno.obs1,pheno.obs2))
ICC<-cov.table[1,2]

affected.threshold.true<-quantile(pheno.true,prevalence.exp)
affected.threshold.obs1<-quantile(pheno.obs1,prevalence.exp)


affected.true<-(pheno.true<affected.threshold.true)*1
affected.obs1<-(pheno.obs1<affected.threshold.obs1)+1


#table(affected.true)
#table(affected.obs1)

true.0<-table(affected.true)[1]
true.1<-table(affected.true)[2]

obs1.0<-table(affected.obs1)[1]
obs1.1<-table(affected.obs1)[2]


tab.true.obs1<-table(affected.true,affected.obs1)

#print(tab.true.obs1)

sensitivity<-tab.true.obs1[2,2]/true.1
specificity<-tab.true.obs1[1,1]/true.0
cat("\n\n SUMMARY\n #######\n")
cat("\n Random Seed\n",seed.val)
cat("\n\n Sample size\n",sample.size)
cat("\n\n Simulated reliability\n",reliability)
cat("\n\n Empirical reliability\n",ICC)
cat("\n\n Required      Required\n Sensitivity   Specificity\n",sensitivity,"   ",specificity,"\n\n")