Hi Gavin,

thank you for the answer. I am aware of the fact that with nMDS it's about
the configuration, and that's exactly my problem: the configuration changes
pretty much when I increase the number of dimensions. As I am trying to go
from a CAT(0) space of trees (see Billera et al on geodesic distance) to an
euclidean space, the required amount of dimensions is not easily determined.
I have to restrict my euclidean space for practical reasons, but I want to
stay as close as possible to the "original" configuration of the trees.
Hence my playing with the dimensions in the nMDS.

I merely commented on the metaMDS as "not really meant for this kind of
data" because of the object that's returned. As you miss the "species"
component in the data, you get warning messages when using procrustes() or
other functions in the vegan package. But you're right. It might be written
for community data, but it is perfectly valid for any kind of distance
matrix.

thanks again for your insights.

Cheers
Joris

On Wed, May 26, 2010 at 9:34 AM, Gavin Simpson <gavin.simpson@ucl.ac.uk>wrote:

> On Tue, 2010-05-25 at 19:00 +0200, Joris Meys wrote:
> > Dear all,
> >
> > I'm running a set of nonparametric MDS analyses, using a wrapper for
> isoMDS,
> > on a 800x800 distance matrix. I noticed that setting the parameter k to
> > larger numbers seriously increases the calculation time. Actually, with
> k=10
> > it calculates already longer than for k=2 and k=5 together. It's now
> > calculating for 6 hours, and counting...
>
> metaMDS will try 'trymax' random starts of isoMDS in an attempt to see
> if convergent solutions are reached. The 10d computation is clearly much
> more complex than fitting rank distances in 2 or even 5 d.
>
> > There is quite a difference between the results using k=2 or k=5 when
> > looking at the first 2 dimensions (logically...). I suspect the same when
> > k=10. Yet, I start asking myself whether this makes sense if I'm only
> using
> > the first 2 dimensions. And I can't think of a formal method to check in
> a
> > nMDS framework how much dimensions are enough. Anybody an idea?
>
> In nMDS the configuration counts, not the axes (as they are themselves
> arbitrary directions --- having one or the other of a x or y
> geographical coordinate isn't much use without the other coordinate if
> you want to find your way to that location - you need both). It makes no
> sense what so ever to compute a 10d nMDS solution if you only want a 2d
> solution for later computations; there is no guarantee that the first
> two "axes" of a 10d nMDS solution will be as good as those from the 2d
> solution. If you only want a 2d solution, concentrate on finding the
> best 2d solution you can using metaMDS.
>
> > I use metaMDS from the vegan package, although it's not really meant to
> be
> > used on these data.
>
> Why do you say that? As long as you turn off a couple of the
> "ecological" helper bits in metaMDS, all it is doing is handling random
> starts of the isoMDS algorithm.
>
> >
> > Cheers
> > Joris
> >
>
> HTH
>
> G
>
> --
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>


-- 
Joris Meys
Statistical Consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

Coupure Links 653
B-9000 Gent

tel : +32 9 264 59 87
Joris.Meys@Ugent.be
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