Thank you, Michael.

I don't think those data for the same group can be treated as repeated
measurements. Let's say I have 1000 observations from group 1 and 1500 obs
from group 2. Some of the 1000 objects of group 1 entered the system at the
same time and may effect each other; same for the other group. It's hard to
measure the heaviness of the dependency.

Even after some twist or transformation, the correlation can be reduced, the
R function "permtest" cannot handle such high sample size. Is there any
other R function I can use?

Thanks,
Wenjin

On Tue, May 24, 2011 at 1:37 AM, Meyners, Michael <meyners.m@pg.com> wrote:

> 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@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
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-
> > guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>

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