The aylmer package has some functionality in this regard
which you may find useful.
In particular, you can use good() to get a feel for the
number of tableaux that are consistent with the
specified marginal totals:
> good(dat2)
[1] 42285210
> good(dat3)
[1] 2.756286e+12
>
HTH
rksh
Søren Faurby wrote:
In an effort to select the most appropriate number of clusters in a
mixture analysis I am comparing the expected and actual membership of
individuals in various clusters using the Fisher?s exact test. I aim
for the model with the lowest possible p-value, but I frequently get
p-values below 2.2e-16 and therefore does not get exact p-values with
standard Fisher?s exact tests in R.
Does anybody know if there is a version of Fisher?s exact test in
any package which can handle lower probabilities, or have other
suggestions as to how I can compare the probabilities?
I am for instance comparing the following two:
dat2<-matrix(c(29,0,29,0,12,0,18,0,0,29,0,16,0,19), nrow=2)
fisher.test(dat2, workspace=30000000)
dat3<-matrix(c(29,0,0,29,0,0,12,0,0,17,0,1,0,29,0,0,15,1,0,0,19),
nrow=3)
fisher.test(dat3, workspace=30000000)
Which both result in p-value < 2.2e-16
Kind regards, Søren
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