[R] help on permutation/randomization test

[Meyners, Michael]

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-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Wenjin Mao
> Sent: Monday, May 23, 2011 20:56
> To: r-help at 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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