Nothing
`mudiff.alc` <-
function(len,alpha1,beta1,alpha2,beta2,n01,n02,level=0.95,equal=TRUE,m=10000,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,equal)[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 <- len
# 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 <= 0) {
found.lower.bound <- TRUE
n1 <- 0
}
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 <- mudiff.samplesize1(alpha1,beta1,alpha2,beta2,n01,n02,equal,n1)
#*********************************
# Here,level is the probability that the true parameter falls
# in the confidence interval
# (thus length changes from a sample to another)
# m is the number of points taken to estimate the integral by Monte Carlo
# The experimental means are Student (see Bernardo & Smith, p. 440)
# Generate m of these values
x1 <- rt(m,2*alpha1)/sqrt(n01*n1/(n01+n1)*alpha1/beta1)
x2 <- rt(m,2*alpha2)/sqrt(n02*n2/(n02+n2)*alpha2/beta2)
# The experimental variances are Gamma-gamma (B & S, p. 440)
# Generate m of these values
b1 <- n1*n01/(n1+n01)*x1^2+2*beta1
ns21 <- rvgg(alpha1+1/2,b1,(n1-1)/2)
b2 <- n2*n02/(n2+n02)*x2^2+2*beta2
ns22 <- rvgg(alpha2+1/2,b2,(n2-1)/2)
bn1 <- beta1+ns21/2+1/2/(n01+n1)*n1*n01*x1^2
bn2 <- beta2+ns22/2+1/2/(n02+n2)*n2*n02*x2^2
cn1 <- (n1+n01)*(alpha1+n1/2)/bn1
cn2 <- (n2+n02)*(alpha2+n2/2)/bn2
dn <- (2*alpha1+n1)/(2*alpha1+n1-2)/cn1+(2*alpha2+n2)/(2*alpha2+n2-2)/cn2
# Return the resulting average length
quantity.to.measure <- 2*mean(sqrt(dn))*qnorm((level+1)/2)
#************************************************************************
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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
#vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
#****************************************************************************
# Once again, define n2
n2 <- mudiff.samplesize1(alpha1,beta1,alpha2,beta2,n01,n02,equal,n1)
#****************************************************************************
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Return
c(n1,n2)
}
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