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Re: st: creating Hierarchical cluster analysis with a different measure of distance

From   [email protected]
To   [email protected]
Subject   Re: st: creating Hierarchical cluster analysis with a different measure of distance
Date   Mon, 18 Jul 2005 09:22:39 -0500

Allan Garland <[email protected]> asks:

> 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.

Stata 9 has the -clustermat- command that performs hierarchical
clustering on a matrix.  Since you have already created a matrix
with the Hoeffding's D distances you can feed that into
-clustermat-.  In the Stata 9 manuals look at "[MV] clustermat"
(starting on page 83 of the MV manual).

Ken Higbee    [email protected]
StataCorp     1-800-STATAPC

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