On May 29, 2015, at 10:02 AM, Cade, Brian wrote: > Wensui: There are the multi-response permutation procedures (MRPP) that > readily test the omnibus hypothesis of no distributional differences among > multiple samples for univariate or multivariate responses. There also are > empirical coverage tests that test a similar hypothesis among multiple > samples but only for univariate responses. Both are included in the USGS > Blossom package for R linked here: > https://www.fort.usgs.gov/products/23735 (not yet distributed via CRAN).
I did not find a link to an actual package at that page nor on any of the others to which it linked. > The MRPP may also be available in other R packages on CRAN (vegan ?). There is an mrpp function in pkg:vegan although its help page description made me think it depended (at least in its default mode) on the squared-deviations from means. I'd suggest using CRAN Task View: Robust Statistical Methods (Maintainer: Martin Maechler) for searching for alternative methods. -- David. > > Brian > > Brian S. Cade, PhD > > U. S. Geological Survey > Fort Collins Science Center > 2150 Centre Ave., Bldg. C > Fort Collins, CO 80526-8818 > > email: ca...@usgs.gov <brian_c...@usgs.gov> > tel: 970 226-9326 > > > On Fri, May 29, 2015 at 10:31 AM, Wensui Liu <liuwen...@gmail.com> wrote: > >> Good morning, All >> I have a stat question not specifically related to the the programming >> language. >> To compare distributional consistency / discrepancy between two >> samples, we usually use kolmogorov-smirnov test, which is implemented >> in R with ks.test() or in SAS with "pro npar1way edf". >> I am wondering if there is any alternative to KS test that could be >> generalized to K-samples. >> >> Thanks and have a nice weekend. >> >> wensui > David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
