CoDA: Clustering Multiple Data Sets

It is not clear whether you need a supervised or an unsupervised model.
Clustering is unsupervised: it will classify compositions in hierarchical
groups regardless the label (countries, regions). If this is what you
intend, you might compute the clustering (hclust) on an euclidean distance
matrix (vegdist) performed across the clr- or ilr-transformed data (both
return the same distances). If you mean a supervised approach, you might
want to explain how groups differ, and/or predict to which group the
composition belongs. To explain, discriminant analysis (packages MASS or
ade4) is (arguably) often a good choice. To predict a category, you might
look at machine learning techniques (see caret package among many others).

Serge-?tienne,

   It would be an unsupervised model. But, more importantly, you let me see
outside the rut into which I wandered. Categorizing streams based on
functional composition, then classifying new streams based on those
categories has not been a completely satisfying solution, and after mailing
my message yesterday I decided to look at a better paradigm. There's a
reason why clustering across multiple compositional data sets has not been
commonly used in the literature I've read.

   Time to step back and examine various multivariate regression approaches;
the intended use of these compositional data is to explain water quality
based on the biota present.

   Thanks for your valuable inputs.

Carpe weekend,

Rich