>>>>> Ross Ihaka writes:
> 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)
> 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().
Yes I know this is a very old mail from Ross ...
I have two questions.
* Is the (already existing) plot.hclust() already a replacement for
plclust()? (I.e., could we add
plclust <- function(...) plot.hclust(...)
for compatibility purposes?)
* I'd be very much interested in some of the functions that are not yet
in the current `mva'. Has there been any work on this one lately? How
can we help?
-k
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