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, [EMAIL PROTECTED] 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 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
