Thanks, Greg. I also considered the clusters. The difficulty is those objects not only enter the system at different time, but may have different duration in the system. Once they have a time overlap in the system, impacts may exist. If splitting into two clusters by setting a time threshold t, I need to drop all objects that enter before time t and leave after time t. The more clusters, the more objects to be dropped that I don't prefer. But two or three clusters may be too small as a sample size. My purpose is to test the difference between two systems.
Back to the R function question. When sample size is large, the full permutation test is infeasible and we have to use randomization test by selecting permutation order randomly. One factor I know that impacts the randomness is the random number generator. I am not sure how well the function "sample" is in randomness. Thanks, Wenjin On Tue, May 24, 2011 at 4:45 PM, Greg Snow <greg.s...@imail.org> wrote: > If the x's that don't enter at the same time can be considered independent > of each other, and only clusters that enter at the same time are dependent, > then you can still do a permutation test by creating clusters with dependent > values within each cluster, but independent between clusters, then permute > the clusters rather than the individual data points. This maintains the > dependency. > > I don't know of any existing functions that will do the whole thing for > you, but this would only be a few lines of R code to do this type of > permutation test. The split function can help with separating the clusters, > sample can do the permutations, and unlist or sapply can be used in > calculating the statistic of interest. > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On Behalf Of Wenjin Mao > Sent: Tuesday, May 24, 2011 11:22 AM > To: Meyners, Michael > Cc: r-help@r-project.org > Subject: Re: [R] help on permutation/randomization test > > 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. > [[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.
