Nothing
klefsjo.ifra.mc<-function (x, alternative="two.sided", exact=FALSE, min.reps=100, max.reps=1000, delta=10^-3)
{
find.ifra = function(x){
x.sort = sort(x)
n = length(x.sort)
D = n*x.sort[1] #find D_{1}
beta = (-1 + (1-(3*n)-(3*(n^2))) + (2*n) + (3*(n^2)) + (n^3) )/6 #beta_{1}
for(i in 2:n){
D = c(D,(n-i+1)*(x.sort[i]-x.sort[i-1])) #alpha_{1}
beta = c(beta, ((2*(i^3)) - (3*(i^2)) + i*(1-(3*n)-(3*(n^2))) + (2*n) + (3*(n^2)) + (n^3) )/6)
}
B = sum(beta*D)/sum(D)
B.star = B*sqrt(210/(n^5))
return(B.star)
}
p.g.mc= function (B.star, n, min.reps=100, max.reps=1000, delta=10^-3)
{
# returns the monte carlo estimate for the probability.
dsn = numeric() #initialize
for(i in 1:min.reps){
dsn = c(dsn,find.ifra(rexp(n)))
}
reps = min.reps
while(reps<=max.reps){
p = length(dsn[dsn>B.star])/reps
dsn = c(dsn,find.ifra(rexp(n)))
if(abs(p-length(dsn[dsn>B.star])/reps)<=delta){
return(p) #if p converges to be w/i delta, then return
}
reps = reps +1
}
print("Warning: reached maximum reps without converging within delta")
return(p)
}
p.l.mc= function (B.star, n, min.reps=100, max.reps=1000, delta=10^-3)
{
# returns the monte carlo estimate for the LESS THAN probability.
dsn = numeric() #initialize
for(i in 1:min.reps){
dsn = c(dsn,find.ifra(rexp(n)))
}
reps = min.reps
while(reps<=max.reps){
p = length(dsn[dsn<B.star])/reps
dsn = c(dsn,find.ifra(rexp(n)))
if(abs(p-length(dsn[dsn<B.star])/reps)<=delta){
return(p) #if p converges to be w/i delta, then return
}
reps = reps +1
}
print("Warning: reached maximum reps without converging within delta")
return(p)
}
# The next three lines are modified from fisher.test to get the correct alternative
# hypotheses. Citation needed?
alternative <- char.expand(alternative, c("two.sided", "dfra", "ifra"))
if (length(alternative) > 1L || is.na(alternative))
stop("alternative must be \"two.sided\", \"dfra\" or \"ifra\"")
B.star = find.ifra(x)
n = length(x)
# large sample approximation
if(n>=9){
if(exact==FALSE){
if(alternative=="dfra"){
p=pnorm(B.star)
}
#dmrl
if(alternative=="ifra"){
p=pnorm(B.star, lower.tail=F)
}
#not equal
if(alternative=="two.sided"){
p=2*pnorm(abs(B.star), lower.tail=F)
}
cat("B*=", B.star, "\n", "p=", p,"\n")
return(list(B.star=B.star,prob=p))
}
}
# Exact Test
#dfr
if(alternative=="dfra"){
p=p.l.mc(B.star,n, min.reps=min.reps, max.reps=max.reps, delta=delta)
}
#ifr
if(alternative=="ifra"){
p=p.g.mc(B.star,n,min.reps=min.reps, max.reps=max.reps, delta=delta)
}
#not equal
if(alternative=="two.sided"){
p=2*min(p.l.mc(B.star,n, min.reps=min.reps, max.reps=max.reps, delta=delta),p.g.mc(B.star,n,min.reps=min.reps, max.reps=max.reps, delta=delta))
}
cat("B*=", B.star, "\n", "p=", p,"\n")
return(list(B.star=B.star,p=p))
}
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