To whomever it may concern (especially Jim Rohlf and David Dubin), I sent an e-mail response to this same broadcast e-mail sent out via the CLASS-L system, but addressed it directly to Jim Rohlf, since, for some reason I misinterpreted this as being an inquiry initiated by him, not one from someone else (Dirk Meurer, I now see) simply passed on by Jim as administrator of CLASS-L. I had thought at the time that perhaps David and Mark Rorvig, prior to Rorvig's tragic and untimely death last year, had put together such a program. While I gather from this message from David that, instead, Michael Trosset (who was also involved in the same DIMACS Workshop on MDS Algorithms I mentioned in that earlier e-mail addressed to Jim) has done so. I would therefore endorse David's recommendation that this software developed by Trosset be used for this purpose. I personally think that, in practice, it seldom makes a great difference whether one uses an MDS procedure that is purely "metric" (as long as you're reasonably careful to make sure the data are transformed, if necessary, to a form as consistent as possible with the assumptions that the procedure is based on-- in particular, most likely that they be DISsimilarities-- which may simply entail reversing the scale, if data are similarities, by, say, subtracting all values from the largest similarity value in the matrix, and possibly, if the particular method assumes RATIO SCALE instead of merely INTERVAL SCALE dissimilarities, that these dissimilarity values are transformed so that it is reasonable to assume that a dissimlarity of zero (0) corresponds to a DISTANCE of zero (0) in the recovered multidimensional representation-- or "nonmetric" (in which the similarities or dissimilarities are assumed only to be monotonic with the proximities-- monotone non-increasing or monotone non-decreasing respectively for the two types-- sim.'s or dissim.'s-- of proximity data). The metric MDS procedures are sufficiently robust under even fairly severe nonlinearities in the monotonic "distance function" transforming proximities into distances in the underlying representation that the solutions are usually almost totally indistinguishable (as long as the same dimensionality is assumed in two analyses, of course), although, as is well known, an orthogonal rotation of a two-way MDS solution (based, as almost all MDS algorithms are, on assumption of Euclidean metric in the underlying space), no matter HOW obtained, is necessary to bring even what are really identical solutions into exact congruence (since Euclidean spaces are defined only up to similarity transforms-- including an orthogonal rotation, as well as translation of the origin and, in some cases a possible uniform dilation resulting in multiplication of all distances by a positive constant-- but the latter two types of transformations are typically resolved by normalization conventions, so generally do not need to be considered). In fact, not only are metric solutions (in the same dimensionality) usually as good as nonmetric ones, there is considerable empirical and theoretical evidence that, for certain types of data, they are in fact BETTER-- being less susceptible to various degeneracies and quasi-degeneracies that can affect many nonmetric MDS analyses, if the data exhibit certain characteristics that are not at all unusual in the case of realistic proximity data from various domains. Best, Doug Carroll. At 03:52 PM 11/12/2002 -0600, David Dubin wrote: >Michael Trosset wrote a program for classical metric MDS that Mark Rorvig >and I used with success on matrices much larger than 400 by 400. But it >doesn't do nonmetric MDS. The program is written in Fortran and requires the >ARPACK libraries. I was able to compile with g77 on Linux and Solaris with >little trouble. > >Dave Dubin > >Dirk Meurer <[log in to unmask]> writes: > > > Dear Listmembers, > > I would like to do a MDS with a 400X400 square, symetric matrix of > > (dis)similaritys. Most MDS-Software is limited to a much smaller amount > > of variables though. I have been told, that SAS might be able to process > > the analyis I need, but this is quite inconvenient for me in terms of > > access to the software, hardware-requirements etc. Could anybody tell > > me, if there is a stand-alone program that can do MDS (preferably > > nonmetric) with a matrix of that size? > > > > Thanks a lot for your help, > > dirk > > > > P.S: My Matrix is not suitable for factor analysis and clustering does > > not produce the results I need. > > ###################################################################### # J. Douglas Carroll, Board of Governors Professor of Management and # #Psychology, Rutgers University, Graduate School of Management, # #Marketing Dept., MEC125, 111 Washington Street, Newark, New Jersey # #07102-3027. Tel.: (973) 353-5814, Fax: (973) 353-5376. # # Home: 14 Forest Drive, Warren, New Jersey 07059-5802. # # Home Phone: (908) 753-6441 or 753-1620, Home Fax: (908) 757-1086. # # E-mail: [log in to unmask] # ######################################################################