R-alpha: Mutivariate Analysis

Ross Ihaka (ihaka@stat.auckland.ac.nz)
Tue, 3 Dec 1996 12:55:01 +1300 (NZDT)


From: Ross Ihaka <ihaka@stat.auckland.ac.nz>
Date: Tue, 3 Dec 1996 12:55:01 +1300 (NZDT)
Message-Id: <199612022355.MAA17838@stat13.stat.auckland.ac.nz>
To: R-testers@stat.math.ethz.ch
Subject: R-alpha: Mutivariate Analysis

I have got a little side-tracked (from graphics) and am putting
together a little multivariate analysis library.  This is just
intended to be a "core" library rather than anything exhaustive.
Mainly it is a matter of putting togther code which already exists at
StatLib.  Here is my present list (only some of which is finished).

 1. Principal Components
    prcomp
	
 2. Clustering
    dist, hclust, plclust, subtree, cutree
    kmeans

 3. Canonical correlations (is this ever used?)
    cancor

 4. Scaling
    cmdscal, sammon, isoscal

 5. Graphics
    (Optimal) profile plots
    biplots
    stars etc
    A bunch of Michael Friendly's stuff converted from SAS

 6. Discriminant analysis
    discr (a real one which takes prior probs and returns posterior ones)

Are there any major omissions here?  (Keep in mind this is not
intended to be exhaustive, just to cover the classical high-points).

I would also like to use the object facility so that printing,
plotting etc is done with generic functions; e.g. plot(prcomp(x))
should produce a scree plot, coef(prcomp(x)) should deliver the
loadings - plclust() should also really be plot.hclust().
	Ross
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