Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space.
This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate a "distance" or dissimilarity between each pair of sets of points. Let's label two of those sets as X,Y, where x is in X and y is in Y. We are interested in defining a distance between X and Y. I have thought of using the following: 1) The Euclidean distance between the centroids of X and Y. Simple and effective but does not give much real information on the actual degree of overlapping. 2) The median of the all the distances between all pairs of points (x,y). Same problem as (1), partially resolved. 3) The proportion of points of X U Y which fall outside the intersection of the convex or concave hulls (defined with a smoothing parameter) of X and Y, i.e. C(X) intersect C(Y). Very complicated, and does not necessarily lead to What do you think? Are there any other approaches worth considering? Kind Regards -- Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct...@york.ac.uk ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.