You could just convert your binary spatial data to numeric 0/1 or -1/1 and
give it to Mclust. That would violate the assumptions of the Gaussian
model in Mclust, so you should be very careful about interpreting the
results. However, if the results are at least as "interesting" as those
you get from a non-model-based hierarchical clustering run, then that could
be an indication that the approach has merit, and then you could
investigate how to build a model-based clustering algorithm that is
appropriate for your data. (I don't think it would be that hard to write
down some equations giving the probability of each presence matrix being
generated for each component of the mixture model, but I don't know how
hard it would be implement the EM search for an appropriate mixture model.)
-- Tony Plate
At Thursday 08:55 PM 12/18/2003 +0000, Jarrod Hadfield wrote:
Dear All,
I have spatial data (presence/absence for 4000 squares) on 250 bird
species and would like to use a model-based clustering technique to test
for species associations. Is there any way of passing a
distance/correlation matrix to mclust as with hclust, rather than the
actual data? Or alternatively, is there a way of getting mclust to handle
binary data?
I'd appreciate any suggestions!
Cheers,
Jarrod
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