Description Usage Arguments Details Value Author(s) See Also Examples
Testversion. Two methods are provided to compute simultaneous confidence intervals for the comparison of several types of odds between several multinomial samples. Asymptotic Waldtype intervals (incl. replacing zero by some small number) as well as a method that computes simultaneous percentile intervals based on samples from the joint Dirichletposterior distribution with vague prior. A separate multinomial distribution is assumed for each row of the contingency table.
1 2 3  multinomORci(Ymat, cmcat=NULL, cmgroup=NULL, cimethod = "DP",
alternative = "two.sided", conf.level = 0.95,
bycomp = FALSE, bychr = " btw ", ...)

Ymat 
a 
cmcat 
a 
cmgroup 
a 
cimethod 

alternative 
single 
conf.level 
single number: the simultaneous confidcence level 
bycomp 
logical, if 
bychr 
character string separating the name of the odds from the name of the between group comparison in the output 
... 
further arguments to be passed to the internal functions: if 
Testversion.
A list with items
SCI 
a 
details 
a list with computational details depending on the 
Frank Schaarschmidt
as.data.frame.multinomORci
, print.multinomORci
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  # Randomized clinical trial 2 treatment groups (injection of saline or sterile water)
# to cure chronic pain after whiplash injuries. Response are 3 (ordered) categories,
# 'no change', 'improved', 'much improved'. Source: Hand, Daly, Lunn, McConway,
# Ostrowski (1994): A handbook of small data sets. Chapman & Hall, Example 124, page 993
dwi < data.frame("no.change"=c(1,14), "improved"=c(9,3), "much.improved"=c(10,3))
rownames(dwi) < c("sterile3", "saline3")
dwi
DP1dwi < multinomORci(Ymat=dwi, cmcat="Dunnett", cmgroup="Tukey", cimethod="DP", BSIM=5000)
DP1dwi
# at logitscale (i.e., not backtransformation)
print(DP1dwi , exp=FALSE)
## Not run:
# Compute asymptotic Waldtype intervals
Waldwbc < multinomORci(Ymat=dwi, cmcat="Dunnett", cmgroup="Tukey", cimethod="Wald")
Waldwbc
print(Waldwbc, exp=FALSE)
## End(Not run)

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