Hi, usually the term MDS is used for methods which operate only on dissimilarity matrices. A similarity matrix s can be easily transformed into a dissimilarity matrix d by taking d <- max(s)-s, which could be considered as kind of a canonical standard to do this.
It seems like the R-MDS methods give errors because your diagonals are larger and should be smaller than anything else for dissimilarities. I am not familiar with kinship matrices. You may try MDS on max(test)-test, but because the diagonals in your matrix are not equal I presume that there is another a bit more subtle standard routine to convert kinship matrices into dissimilarities, maybe something like (raw, not R) d(i,j)=1-s(i,j)^2/(s(i,i)s(j,j)). Did you consider the Statistica manual? It should tell you... Hope this is of any help, Christian On 26 Feb 2004, Federico Calboli wrote: > Dear All, > > I am in the somewhat unfortunate position of having to reproduce the > results previously obtained from (non-metric?) MDS on a "kinship" matrix > using Statistica. A kinship matrix measures affinity between groups, and > has its maximum values on the diagonal. > > Apparently, starting with a nxn kinship matrix, all it was needed to do > was to feed it to Statistica flagging that the matrix was NOT a distance > matrix but a kinship one. If Statistica transformed the kinship matrix > into a distance one (how?) is anybody's guess. > > A quick search immediately showed that a multidimensional scaling is > done on a distance matrix. See for instance: > MASS4, pg 304 > "Elements of computational statistics", Jentle, pg 122 > Edwards and Oman's article, page 2-7 R-News 3/3 > > The fact that Statistica happily perform MDS on a "kinship" matrix is > puzzling. Indeed, I would expect errors, as in the following toy > example, without transforming the kinship matrix to distances: > > > test > V1 V2 V3 V4 V5 > 1 0.198716340 0.003612042 0.011926851 0.019737349 0.015021053 > 2 0.003612042 0.066742885 0.013809924 0.005121996 0.011175845 > 3 0.011926851 0.013809924 0.197337389 0.013893087 0.006405424 > 4 0.019737349 0.005121996 0.013893087 0.216047450 0.006218477 > 5 0.015021053 0.011175845 0.006405424 0.006218477 0.118812936 > > cmdscale(test) > [,1] [,2] > V1 NaN NaN > V2 NaN NaN > V3 NaN NaN > V4 NaN NaN > V5 NaN NaN > Warning messages: > 1: some of the first 2 eigenvalues are < 0 in: cmdscale(test) > 2: NaNs produced in: sqrt(ev) > > isoMDS(test) > Error in isoMDS(test) : NAs/Infs not allowed in d > > sammon(test) > Error in sammon(test) : initial configuration must be complete > In addition: Warning messages: > 1: some of the first 2 eigenvalues are < 0 in: cmdscale(d, k) > 2: NaNs produced in: sqrt(ev) > > > The colleagues who used the above routine are unable to tell me with > certainty whether Statistica used metric/non metric scaling, and if non > metric whether a Kruskall or a Sammon scaling. > > In any case, I would simply like to ask the memebers of the list if I am > correct in thinking that MDS can ONLY be performed on a distance matrix, > and I can therefore reasonably expect that some form of transformation > to a distance matrix has been performed by Statistica prior to the MDS. > It would at least be a first step to understand what exactly Statistica > did with the data. > > Regards, > > Federico Calboli > -- > > > > ================================= > > Federico C. F. Calboli > > Dipartimento di Biologia > Via Selmi 3 > 40126 Bologna > Italy > > tel (+39) 051 209 4187 > fax (+39) 051 251 208 > > f.calboli at ucl.ac.uk > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg [EMAIL PROTECTED], http://www.math.uni-hamburg.de/home/hennig/ ####################################################################### ich empfehle www.boag-online.de ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
