I suspect you need to give more information/background on the data (though this 
is not primarily an R-related question; you might want to try other resources 
instead). Unless I'm missing something here, I cannot think of ANY reasonable 
test: A permutation (using permtest or anything else) would destroy the 
correlation structure and hence give invalid results, and the assumptions of 
parametric tests are violated as well. Basically, you only have two 
observations, one for each group; with some good will you might consider these 
as repeated measurements, but still on the same subject or whatsoever. Hence no 
way to discriminate the subject from a treatment effect. There is not enough 
data to permute or to rely a statistical test on. So unless you can get rid of 
the dependency within groups (or at least reasonably assume observations to be 
independent), I'm not very optimistic...
HTH, Michael

> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Wenjin Mao
> Sent: Monday, May 23, 2011 20:56
> To: r-help@r-project.org
> Subject: [R] help on permutation/randomization test
> 
> Hi,
> 
> I have two groups of data of different size:
>    group A: x1, x2, ...., x_n;
>    group B: y1, y2, ...., y_m; (m is not equal to n)
> 
> The two groups are independent but observations within each group are
> not independent,
>  i.e., x1, x2, ..., x_n are not independent; but x's are independent
> from y's
> 
> I wonder if randomization test is still applicable to this case. Does
> R have any function that can do this test for large m and n? I notice
> that "permtest" can only handle small (m+n<22) samples.
> 
> Thank you very much,
> Wenjin
> 
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