I am not sure, but it seems that your problem is related to
compositional data analysis. You can probably use Aitchison distance to
estimate separation between proportions.
Take a (free) look at:
On Tue, 13 May 2014, Zbigniew Ziembik wrote:
or (commercial):
Aitchison, J. 2003. The Statistical Analysis of Compositional Data. The
Blackburn Press.
There's also: Analyzing Compositional Data with R by van den Boogaart, K.
Gerald,Tolosana-Delgado, Raimon. Published by Springer in their
Typical dissimilarity indices are of form difference/adjustment, where the
adjustment takes care of forcing the index to the range 0..1, and handles
varying total abundances / richnesses. If you have proportional data, you may
not need the adjustment at all, but you can just use any index. That
Typical dissimilarity indices are of form difference/adjustment, where the
adjustment takes care of forcing the index to the range 0..1, and handles
varying total abundances / richnesses. If you have proportional data, you may
not need the adjustment at all, but you can just use any index. That
I would also suggest to give a try to the Aitchison distance. To do so, you can
use the âcompositionsâ package. You transform the proportions to centered
log-ratios or isometric log-ratios (clr and ilr functions, respectively), then
compute the Euclidean distance through transformed data -
