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.Snow@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-bounces@r-project.org [mailto:r-help-bounces@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 <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.
> >
>
>         [[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]]

