On Thu, 21 Jun 2007, Steve Antos wrote:
> What are the limitations on size of matrix for MDS functions?
MDS works with a dissimilarity, not a matrix (neither conceptually nor in
most R implementations, which typically use an object of class
"dist").
It is better to think in terms of the number of objects 'n' and the
number
of dimensions of the representation (which I guess you mean as 2).
There are O(n^2) dissimilarities to be considered, and most of the
algorithms appear to be slightly superlinear in that number. n=1000 runs
in isoMDS in about a minute on my laptop, using about 75Mb of memory, and
about 10 secs in sammon or cmdscale. (Highly non-Euclidean
dissimilarities are likely to be slower.)
Even 1000 objects is a lot to be considering for what is primarily a
visualization technique.
Cruder forms of MDS such as Kohonen mapping are able to handle much larger
datasets (but reveal less about them).
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595