On Wed, 2010-05-26 at 14:25 +0200, Joris Meys wrote:
> 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
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 di
Hi Michael,
thanks for your answer. Indeed, with a 100x100 matrix it runs even pretty
fast with k=30. But as with a lot of things in R, there is a
disproportionate rise in the calculation time once you exceed a certain size
limit on your matrices. In the end, it ran about 8 hours for my complete
m
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
Hi Joris,
On Tue, May 25, 2010 at 1:00 PM, 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,
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
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