Thanks for that, just to confirm I therefore need to use:
#if
#geogdist  is a geographic distance matrix
#gen_dist is a genetic distance matrix
#env_var are environmental variables

mypcnm<-pcnm(geogdist)


mydbRDA<-capscale(gen_dist~env_var+Condition(mypcnm$vectors))

cheers


Nevil





On 22/09/2010 1:34 AM, Jari Oksanen wrote:
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


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