R help - Dec 2008 - why stress value remains so high after invoking of metaMDS

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

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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