Hi Phil, Are you using metaMDS in the vegan package? This allows you to determine the number of random starts, and selects the best. It might help. Hank Stevens > Dear Phil, > > I don't have experiences with Minissa but I know that isoMDS is bad in > some situations. I have even seen situations with non-metric > dissimilarities in which the classical MDS was preferable. > > Some alternatives that you have: > 1) Try to start isoMDS from other initial configurations (by default, it > starts from the classical solution). > 2) Try sammon mapping (command should be "sammon"). > 3) Have a look at XGvis/GGvis (which may be part of XGobi/GGobi). These > are not directly part of R but have R interfaces. They allow you to toy > around quite a lot with different algorithms, stress functions (the > isoMDS stress is not necessarily what you want) and initial > configurations so that you can find a better solution and understand your > data better. Unfortunately I don't have the time to give you more detail, > but google for it (or somebody else will tell you more). > > Best, > Christian > > > On Tue, 13 Feb 2007, Philip Leifeld wrote: > >> Dear useRs, >> >> last week I asked you about a problem related to isoMDS. It turned >> out that in my case isoMDS was trapped. Nonetheless, I still have >> some problems with other data sets. Therefore I would like to know if >> anyone here has experience with how well isoMDS performs in >> comparison to other non-metric MDS routines, like Minissa. >> >> I have the feeling that for large data sets with a high stress value >> (e.g. around 0.20) in cases where the intrinsic dimensionality of the >> data cannot be significantly reduced without considerably increasing >> stress, isoMDS performs worse (and yields a stress value of 0.31 in >> my example), while solutions tend to be similar for better fits and >> lower intrinsic dimensionality. I tried this on another data set >> where isoMDS yields a stress value of 0.19 and Minissa a stress value >> of 0.14. >> >> Now the latter would still be considered a fair solution by some >> people while the former indicates a poor fit regardless of how strict >> your judgment is. I generally prefer using R over mixing with >> different programs, so it would be nice if results were of comparable >> quality... >> >> Cheers >> >> Phil >> >> ______________________________________________ >> 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. >> > > *** --- *** > Christian Hennig > University College London, Department of Statistical Science > Gower St., London WC1E 6BT, phone +44 207 679 1698 > [EMAIL PROTECTED], www.homepages.ucl.ac.uk/~ucakche > > ______________________________________________ > 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. >
______________________________________________ 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.
