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 performance? Or is there
any other better methods to do MDS?
-- 
Wu Chen
Information Management School, WHU

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