Re: [R] Difference between prcomp and cmdscale

From: michael watson (IAH-C) <michael.watson_at_bbsrc.ac.uk>
Date: Thu, 14 Jun 2007 13:52:40 +0100

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: r-help-bounces_at_stat.math.ethz.ch
[mailto:r-help-bounces_at_stat.math.ethz.ch] On Behalf Of Mark Difford Sent: 14 June 2007 12:49
To: r-help_at_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}}



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