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 <meyner...@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-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 > > > > ______________________________________________ > > 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. > [[alternative HTML version deleted]] ______________________________________________ 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.
