On Mon, Apr 14, 2008 at 4:47 PM, Gavin Simpson <[EMAIL PROTECTED]> wrote:
> Note that the default is to produce a bray-curtis dissimilarity matrix > from the input species data. As such, I reproduce this dissimilarity > matrix as arg 1 to cor and then take the Euclidean distances of the > coordinates on nMDS axes 1:2 (this example is a 2D solution but scales > to n-dimensions) as the second argument to cor: > > cor(vegdist(dune), dist(sol$points))^2 > > In this example, the "R^2" is 0.899222 > > If you already have jaccard for your species data, then substitute this > for 'vegdist(dune)' and sol for whatever your nMDS object is called. Good point. I forgot to clarify that you should use the same dissimilarity metric for your original data that was used in constructing the ordination. Euclidean distances should be used for the NMDS configuration, though. > However, this assumes a linear relationship between the original > distances and the nMDS distances. The relationship need not be linear, > just monotonic if I recall the details correctly. If you (the original querent) are interested in the monotonic rather than strictly linear relationship, you could use the "spearman" option to cor(). Sarah -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ 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.
