Thanks for the reply Warren. I'm making hierarchical clusters based on
euclidian distances and linking using Ward's method, so I don't think I'm
warping my space. I think my transformations of the raw matrix are also
reasonable. On the otherhand, I'm unversed in the triangle inequality you
mentioned, so I can't comment on this.
I did get a chance to run the question by a local statistician, who
thought that the zeros in my data (about 87% of which is zero) is causing
the test statistic to degrade i.e. takes on only a few values, so that
only extreme cases turn out to be significant. An example of this she
gave was testing on a binomial distribution with only 4 points - in this
case only 1 outcome is 'significant'. Unfortunately, she couldn't
recomend a better means of testing veg clusters, stating the well known
truth that clustering is an art, and generally not subjected to testing.
On Tue, 19 May 1998, Warren Sarle wrote:
>> If you are using the same distance matrix for the cluster analysis
> and the MRPP, and your data set is not tiny, then you should indeed
> get wildly "significant" results. I would be surprised to see a
> p-value over .0001. If you are using different distance matrices,
> anything could happen. Perhaps weird things could also happen if
> you used an extremely non-Euclidean distance matrix; for example,
> something that had gross violations of the triangle inequality.