That's exactly what ordination is for (not clustering). I'd try principal coordinates analysis, or non-metric multidimensional scaling, depending on whether the dissimilarity you'v been given is metric or nonmetric.
There are implementations of both in the ecodist package, and in various other packages as well, so you have lots of choice. Sarah On Thu, Dec 15, 2011 at 1:08 PM, Lorenzo Isella <lorenzo.ise...@gmail.com> wrote: > Dear All, > I am struggling with the following problem: I am given a NxN symmetric > matrix P ( P[i,i]=0, i=1...N and P[i,j]>0 for i!=j) which stands for the > relative distances of N points. > I would like use it to get the coordinates of the N points in a 2D plane. Of > course, the solution is not unique (given one solution, I can translate or > rotate all the points by the same amount and generate another solution), but > any correct solution will do for me. > Any idea about how I can achieve that? Is there any clustering package that > can help me? > Many thanks. > > Lorenzo > -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ R-help@r-project.org 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.
