In article <Pine.OSF.3.96.980518070912.21248B-100000 at axe.humboldt.edu>,
Curt Seeliger writes:
|> Folks, I'm currently at a loss. I'm developing vegetation types using
|> standard hierarchic clustering techniques (Euclidian distance, Ward's
|> method). I'm testing these groups using Multiple-Response Permutation
|> Procedures, which estimates the likelyhood of finding tighter clusters.
|> MRPP results in p-values ranging from 0.0002 to 0.90 for clusters derived
|> in this manner, depending on how I transform the data matrix before
|>|> My problem is that groupings which are statistically significant do not
|> appear to be of any ecological meaning. I'm fairly boggled by these
|> results, as I would assume that any sort of clustering method would always
|> produce groupings which are at least better than random (p < 0.50), but
|> this does not seem to be the case.
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.
Warren S. Sarle SAS Institute Inc. The opinions expressed here
saswss at unx.sas.com SAS Campus Drive are mine and not necessarily
(919) 677-8000 Cary, NC 27513, USA those of SAS Institute.