Nothing
`mudiff.mblmodwoc` <-
function(len,alpha1,beta1,alpha2,beta2,level=0.95,worst.level=0.95,m=50000,mcs=3)
{
min.for.possible.return <- 2^ceiling(1.5*mcs)
# If we always allow a return, there is a risk of making bad steps
# when we are close to the answer.
# Thus, we should not allow any return once some arbitrary 'step' (which is
# 'min.for.possible.return') is reached.
#vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
#**************************************************************************
# Define initial step
# (as a function of any frequentist sample size estimate)
step <- ceiling(log(mudiff.freq(len,alpha1/beta1,alpha2/beta2,level)[1])/log(2))
# Also define the threshold to cross for the quantity under study (the
# length or the coverage probability of an HPD region)
threshold <- level
# and define a factor, which is +/- 1, depending on if the quantity under
# study is (almost) surely too large or too small when making no
# observations [-1 if the quantity to measure is DEcreasing with n
# +1 if the quantity to measure is INcreasing with n]
#
# [ -1 if threshold_len, +1 if thresold_level ]
factor <- +1
#**************************************************************************
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
quantity.to.measure <- ifelse(factor == +1,0,2*threshold)
step <- 2^step
history.ns <- 0
history.steps <- 0
# history.cons.steps_0
n1 <- 0
max.cons.steps.same.dir <- mcs
found.upper.bound <- FALSE
possible.to.move.back <- TRUE
cons.steps.same.dir <- 0
direction <- +1
while(step>=1)
{
while(sign(factor*(threshold-quantity.to.measure)) == direction && step >= 1)
{
step[found.upper.bound] <- max(1,step/2)
possible.to.move.back[step < min.for.possible.return &&
found.upper.bound] <- FALSE
n1 <- n1+direction*step
if(n1 <= 2) {
found.lower.bound <- TRUE
n1 <- 2
}
cons.steps.same.dir <- cons.steps.same.dir+1
history.ns <- c(n1,history.ns)
history.steps <- c(step*direction,history.steps)
#vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
#************************************************************************
# Define n2 from n1
n2 <- n1
#*********************************
# Let total.var=n1*s21/n1/(n1-1)+n2*s22/n2/(n2-1)
total.var <- rgg(m,alpha1,2*beta1/n1/(n1-1),(n1-1)/2)+
rgg(m,alpha2,2*beta2/n2/(n2-1),(n2-1)/2)
worst.var <- sort(total.var)[worst.level*m]
quantity.to.measure <- 2*pnorm(len/2/sqrt(worst.var))-1
#************************************************************************
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
if(found.upper.bound &&
cons.steps.same.dir == max.cons.steps.same.dir+1 &&
possible.to.move.back)
{
if(sign(factor*(threshold-quantity.to.measure)) == direction)
{
# There was (most likely) a mistake, look for n's in the other direction
at.n <- seq(along=history.ns)[history.ns == n1 ]
hs.an <- history.steps[at.n]
step <- abs(hs.an[sign(hs.an) != direction][1])
cons.steps.same.dir <- 0
# and if there has never been a step coming from the other direction
step[is.na(step)] <- max(abs(hs.an))
}
else
{
# There was (most likely) no mistake; keep looking around the same n's
direction <- -direction
cons.steps.same.dir <- 0
}
}
if(found.upper.bound &&
cons.steps.same.dir==max.cons.steps.same.dir &&
sign(factor*(threshold-quantity.to.measure))==direction &&
possible.to.move.back)
{
step <- 2*step
}
}
found.upper.bound <- TRUE
direction <- -direction
cons.steps.same.dir <- 0
step[step==1] <- 0
}
direction[n1==0] <- 0
n1[direction==+1] <- n1+1
# Return
c(n1,n1)
}
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