R help - May 2010 - Calculation time of isoMDS and the optimal number of dimensions

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...

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?

I use metaMDS from the vegan package, although it's not really meant to be
used on these data.

Cheers
Joris

-- 
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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Hi Joris,

On Tue, May 25, 2010 at 1:00 PM, Joris Meys <jorismeys at gmail.com>
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...
Seems like a long time, I have a 100x100 matrix that takes about 40
secs to run with k=10. What is the wrapper function doing?
>
> 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?
You might want to look at the nmds.min() function in the ecodist
package, which seeks to minimize stress. Out of curiosity, do you
often use 10 dimensional solutions in your field of study?

Hope this helps,
Michael
> I use metaMDS from the vegan package, although it's not really meant to
be
> used on these data.
>
> Cheers
> Joris
>
> --
> 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 at Ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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> PLEASE do read the posting guide
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>


-- 
Michael Denslow

I.W. Carpenter Jr. Herbarium [BOON]
Department of Biology
Appalachian State University
Boone, North Carolina U.S.A.
-- AND --
Communications Manager
Southeast Regional Network of Expertise and Collections
sernec.org

36.214177, -81.681480 +/- 3103 meters

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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