If cm is a similarity matrix, why are you taking its Euclidean distance? (The usage I'm familiar with has similarity as a pairwise measure of association.)
Otherwise, if you feel the stress is too high, that implies that a 2-dimensional solution is inadequate for your data and you should consider more dimensions. Sarah PS It's not necessary to repost; not everyone is here 24 hours a day, especially over what for most of Europe and the US are holidays. A question may sit for a few hours (or *gasp* days) until someone with the right expertise checks in. On Tue, Dec 30, 2008 at 1:06 AM, Wu Chen <gemini...@gmail.com> wrote: > Hello everyone! > > metaMDS(cm, distance = "euclidean", k = 2, trymax = 50, autotransform > =TRUE, trace = 1, plot = T) > (cm is a similarity matrix, in which values are positive integers or 0) > > I use this command to run NMDS on my matrix "cm". But the stress is very > high after analysis. About 14. > Actually, there is no improvment comparing with using isoMDS. > > cd<-dist(cm,method="euclidean") > loc<-isoMDS(cd,tol = 1e-10,trace=T) > > Is there parameters that I can change to improve the performance? Or is > there any other better methods to do MDS? > -- > Wu Chen > Information Management School, WHU > -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ 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.
