Hi Michael, Doubtless Professor Ripley did; but it helps to put your back into it. Long ago Gower (1966) drew attention to the links between PCA and classical scaling. It took me a few seconds to find this:
http://www.garfield.library.upenn.edu/classics1980/A1980JJ08200001.pdf Of course, I knew about Gower. But I knew about Gower because I had done the _basic_ research on these methods. And that was my point. In a later paper Gower argued that classical scaling extended, and was more powerful than, PCA. However, classical scaling operates on [a matrix of] similarities between observations/individuals/rows, whereas PCA operates on [a matrix of] similarities between variables/descriptors/columns. This means that in classical scaling the axes cannot be interpreted; often one does a PCA to get at these. HTH, bestR, Mark. michael watson (IAH-C) wrote: > > Hi Mark > > I think Brian Ripley answered this most effectively and succinctly. I > did actually do quite a bit of googling and searching of the R help > before posting, and whilst there is quite a lot on each topic > individually, I failed to find articles that compare and contrast PCA > and MDS. If you know of any, of course I would be happy to read them. > > Many thanks > Mick > > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Mark Difford > Sent: 14 June 2007 12:49 > To: r-help@stat.math.ethz.ch > Subject: Re: [R] Difference between prcomp and cmdscale > > > Michael, > > Why should that confuse you? Have you tried reading some of the > literature > on these methods? There's plenty about them on the Net (Wiki's often a > goodish place to start)---and even in R, if you're prepared to look ;). > > BestR, > Mark. > > > michael watson (IAH-C) wrote: >> >> I'm looking for someone to explain the difference between these >> procedures. The function prcomp() does principal components anaylsis, >> and the function cmdscale() does classical multi-dimensional scaling >> (also called principal coordinates analysis). >> >> My confusion stems from the fact that they give very similar results: >> >> my.d <- matrix(rnorm(50), ncol=5) >> rownames(my.d) <- paste("c", 1:10, sep="") >> # prcomp >> prc <- prcomp(my.d) >> # cmdscale >> mds <- cmdscale(dist(my.d)) >> cor(prc$x[,1], mds[,1]) # produces 1 or -1 >> cor(prc$x[,2], mds[,2]) # produces 1 or -1 >> >> Presumably, under the defaults for these commands in R, they carry out >> the same (or very similar) procedures? >> >> Thanks >> Mick >> >> The information contained in this message may be\ confiden...{{dropped}} > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/Difference-between-prcomp-and-cmdscale-tf3920408.html#a11120608 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
