why stress value remains so high after invoking of metaMDS
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 <geminiwhu at 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