On 21/09/10 17:40 PM, "Nevil Amos" <nevil.a...@gmail.com> wrote:
> I am trying to use the cca/rda/capscale functions in vegan to analyse > genetic distance data ( provided as a dist object calculated using > dist.genpop in package adegenet) with geographic distance partialled out > ( provided as a distance object using dist function in veganthis method > is attempting to follow the method used by Geffen et al 2004 as > suggested by Legendre and . FORTIN (2010). > > I cannot see how to introduce the Conditioning ( partialled) second dist > matrix. as you can see from the code snippet below, the two dist > objects are of the same dimensions. - I get an error using capscale: > Error in qr.fitted(Q, Xbar) : > 'qr' and 'y' must have the same number of rows > or cca > Error in weighted.mean.default(newX[, i], ...) : > 'x' and 'w' must have the same length > when using a conditioning distance object instead of a variable (Clade) > of the same length as the constraints ( Latitude and Longitude) > > I would be grateful, for any pointers on this, ie which test is the > appropriate one to use ( I believe capscale since it is "similar to > distance-based redundancy analysis (Legendre & Anderson 1999)") and > whether this test is indeed equivalent to the approach suggested by > Legendre &Fortin, (Geffen et al used DISTLM). > Nevil, You cannot use cca() for dissimilarity data. If you have dissimilarity data, you must use capscale() which runs db-RDA. Even there, your constraints (variables on the right hand side of the formula) must be rectangular data and not dissimilarities. AFAIK, people have changed their dissimilarities into a PCNM structure when they want to partial out the distance effect. That is one of the few original possibilities since data must be rectangular (rows and columns). Cheers, jari oksanen ______________________________________________ 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.
