st: creating Hierarchical cluster analysis with a different measure ofdistance

[Allan Garland <agar5858@cox.net>]

Is there a relatively easy way to implement a hierarchical cluster 
analysis in Stata 9 on the variables (not the observations), using a 
different measure of distance between the variables?

The "clv" program appears to use an approach to assessing the distances 
similar to that of principal components.  Using the built-in cluster 
commands on the variables requires transposing the rows and columns of 
the data.  I looked at that routine and it wasn't simple enough (for me) 
to see how to alter Jean-Benoit Hardouin's code to do this (I'm an 
intermediate at program writing). 
In any case, what I want to implement in Stata is what Frank Harrell 
describes in his textbook (FE Harrell Jr. (2001). Regression Modeling 
Strategies. New York, Springer) where he promotes the value of doing HCA 
(for data reduction) using as a measure of distance/similarity between 
variables the Hoeffding's D (W Hoeffding. A Non-Parametric Test of 
Independence. Annals of Mathematical Statistics 19(4):546-557, 1948).  
I've written code that calculates the matrix of D's between all pairs of 
variables, and am HOPING that someone can point me in the direction of  
Stata programming code that will let me do something simple --- i.e. 
just plug the matrix of D's in and thus obtain a HCA.

Any ideas would be most welcome.

Allan Garland


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