Meaningful results might be obtained by representing a k-level categorical 
variable by k zero-one dummy variables, to be treated as continuous. This 
is implemented for variable clustering in the Design package.  I haven't 
thought about how that approach may work (or otherwise) with clustering of 
sites (etc.). 

r-sig-ecology-bounces@r-project.org wrote on 12/29/2010 02:53:48 PM:

> From:
> 
> Jonathan Hughes <jonathan.hughes.10@live.com>
> 
> To:
> 
> <r-sig-ecology@r-project.org>
> 
> Date:
> 
> 12/29/2010 02:54 PM
> 
> Subject:
> 
> [R-sig-eco] cluster analysis using both continuous and discrete 
variables
> 
> Sent by:
> 
> r-sig-ecology-bounces@r-project.org
> 
> 
> 
> Hello,
> 
> I need to do a cluster analysis with a dataset that includes both 
> continuous and discrete variables. I can't get around the problem of
> computing a meaningful distance matrix with such a dataset. I was 
> wondering if any of you guys has dealt with this issue and what kind
> of function you found useful.
> 
> Thanks!
> 
> Jonathan
> 
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> 
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