Hola! see below. On Sat, Oct 3, 2009 at 2:45 PM, David Winsemius <dwinsem...@comcast.net> wrote: > Do you have a citation for that statement? I cannot convince myself that it > should be true.
OK. that took some time, since I have no nonparametrics book with me, but it is a fairly standard assumption the friedman.test shares with wilcox.test and others. One online reference giving this is: http://www.mathworks.com/access/helpdesk/help/toolbox/stats/index.html?/access/helpdesk/help/toolbox/stats/friedman.html&http://www.google.cl/search?q=assumptions+of+friedman+test&ie=utf-8&oe=utf-8&aq=t&rls=com.ubuntu:en-US:unofficial&client=firefox-a specifically: "Friedman's test makes the following assumptions about the data in X: * All data come from populations having the same continuous distribution, apart from possibly different locations due to column and row effects. * All observations are mutually independent. " This is also easy to investigate by simulation in R: I did: > A[, 1] <- rnorm(100, 0, 1) > A[, 2] <- rnorm(100, 0, 5) > A[, 3] <- rnorm(100, 0, 500) > friedman.test(A) Friedman rank sum test data: A Friedman chi-squared = 2.96, df = 2, p-value = 0.2276 which surprised me! This test seems to be somewhat robust against variance heterogeneity ???, but that case is not included in the usual theory. Kjetil > > After looking at the CRAN Task View, I would suggest the OP look at > rlm(MASS) or lmrob(robustbase). > > -- > David > > On Oct 2, 2009, at 11:05 AM, Kjetil Halvorsen wrote: > >> On Fri, Oct 2, 2009 at 8:45 AM, David Winsemius <dwinsem...@comcast.net> >> wrote: >>> >>> There are multiple routes to "robust" statistics, but the quick answer to >>> this question is probably friedman.test >> >> I don't think friedman.test is robust to variance heterogeneity. It is >> only robust to >> non-normality. >> >> Kjetil >> >> >> >>> >>> I seem to remember a CRAN Task View on the area of Robust Statistics. >>> >>> -- >>> David Winsemius >>> >>> >>> On Oct 2, 2009, at 3:05 AM, Maike Luhmann wrote: >>> >>>> Dear list members, >>>> >>>> I am looking for an alternative function for a two-way ANOVA in the case >>>> of >>>> variance heterogeneity. For one-way ANOVA, I found oneway.test(), but I >>>> didn't find anything alike for two-way ANOVA. Does anyone have a >>>> suggestion? >>>> >>>> Thank you! >>>> >>>> Maike Luhmann >>>> Freie Universität Berlin >>> >>> ______________________________________________ >>> 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. >>> > > David Winsemius, MD > Heritage Laboratories > West Hartford, CT > > ______________________________________________ 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.