On Mon, Oct 19, 2009 at 10:12 AM, Jan Hanspach <jan.hansp...@ufz.de> wrote: > Dear Sarah, > that sounds good to me, thanks! > > Still, I have two more questions: > > The variable fitting with vf() does only work when I "dummy-transform" my > categorical variables. Are the derived correlations then still comparable to > the Gower distance - PCoA scores where my categorical variables where > defined as such?
I think it only makes sense with quantitative variables. There's no "direction" to categorical variables, so how could you fit one? Usually with categorical variables I plot the ordination with different symbols to denote the class of the categorical variables, and look at pattern that way. > Is it reasonable to do more than the correlations with two axes with vf(), > i.e. after calculating the correlation for the first and the second axis > doing the same for the third and the forth? I guess this is a very naive > question, but shouldn't this work for the p-Values since they are calculated > with a permutation procedure? You're fitting the variables to that ordination diagram. It's entirely possible that the best fit for dimensions 3 and 4 of a four-dimensional fit are not the same as the best fit of axis 3 and 4 by themselves. So it depends on your question. Alternatively, I think the vegan package has a more sophisticated vector- fitting function. Sarah -- Sarah Goslee http://www.functionaldiversity.org _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology