R help - Mar 2013 - Testing for significance of overlap in three sets - mantelhaen test?

Hi,

My apologies for the naive question!

I have three overlapping sets and I want to find the probability of finding
a larger/greater intersection for 'A intersect B intersect C' (in the
example below, I want to find the probability of finding more than 135
elements that are common in sets A, B & C). For a two set problem, I guess
I would do a Fisher or chi-square test. Here is what I have attempted so
far:

#################

### Prepare a 3 way contingency table:
mytable <- array(c(135,116,385,6256,
                    48,97,274,9555),
                  dim = c(2,2,2),
                  dimnames = list(
                    Is_C = c('Yes','No'),
                    Is_B = c('Yes','No'),
                    Is_A = c('Yes','No')))

## test
mantelhaen.test(myrabbit, exact = TRUE, alternative = "greater")

###################### end code

Is this the right test (alongwith the current parameters) to determine what
I want or is there a more appropriate test for this?



many thanks!!

	[[alternative HTML version deleted]]

As this seems to be a statistics, not an R, question, it is off topic
here. Post on a statistics list like stats.stackexchange.com instead.

-- Bert

On Tue, Mar 12, 2013 at 6:22 AM, Brian Smith <bsmith030465 at gmail.com>
wrote:
> Hi,
>
> My apologies for the naive question!
>
> I have three overlapping sets and I want to find the probability of finding
> a larger/greater intersection for 'A intersect B intersect C' (in
the
> example below, I want to find the probability of finding more than 135
> elements that are common in sets A, B & C). For a two set problem, I
guess
> I would do a Fisher or chi-square test. Here is what I have attempted so
> far:
>
> #################
>
> ### Prepare a 3 way contingency table:
> mytable <- array(c(135,116,385,6256,
>                     48,97,274,9555),
>                   dim = c(2,2,2),
>                   dimnames = list(
>                     Is_C = c('Yes','No'),
>                     Is_B = c('Yes','No'),
>                     Is_A = c('Yes','No')))
>
> ## test
> mantelhaen.test(myrabbit, exact = TRUE, alternative = "greater")
>
> ###################### end code
>
> Is this the right test (alongwith the current parameters) to determine what
> I want or is there a more appropriate test for this?
>
>
>
> many thanks!!
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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