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 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- r-testers mailing list -- For info or help, send "info" or "help", To [un]subscribe, send "[un]subscribe" (in the "body", not the subject !) To: r-testers-request@stat.math.ethz.ch =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-