mv.SM {CLAST} | R Documentation |
Calculates mean value of a provided vector of upper limits as a function of p
mv.SM(obj, lims = NULL, p = NULL, B = 99, offset = TRUE, wgt = TRUE)
obj |
list with components $S, $N, $count and optionally $lims, typically the output of sample.space.SM |
lims |
if not a component of obj |
p |
vector of values of p at which to calculate mean value |
B |
number of evenly spaced values of p if not provided |
offset |
if TRUE then ML mean value is subtracted |
wgt |
if TRUE than assign zero probability weight to extreme limits of 1 or 0. |
list with elements $x (containing grid of B values of probability) and $y (containing corresponding mean values)
Chris J. Lloyd
n=c(5,6,5,9)
a=c(2,4,5,12)
b=c(5,9,11,13)
# Enumerate all possible elements of the sufficiency reduced samples
# space i.e. all values of S and M. Also listed are the counts and
# subcounts of these outcomes, the test decision and the vector
# n, a, and b in $design
data.SM=sample.space.SM(n,a,b) # There are 26 elements.
# Calculate all approximate LR upper limits for these 26 outcomes.
all.LR.high=LR.stats.SM(data.SM,type="upper")$lims
# Calculate all approximate LR upper limits for these 26 outcomes.
all.LR.low=LR.stats.SM(data.SM,type="lower")$lims
# Calculate the mean values of these lims as a function of p
mv.high=mv.SM(data.SM,all.LR.high,p=(1:99)/100,offset=FALSE)
mv.low=mv.SM(data.SM,all.LR.low,p=(1:99)/100,offset=FALSE)
plot(c(0,1),c(0,1),ylab="mean value",xlab="p",type="n")
lines(mv.high)
lines(mv.low)
abline(0,1,lty=3)
title(main="Mean value of upper and lower limits by p")