Thanks both, Im happy to have my thoughts confirmed.

Gavin, I ran all of the variables I included through ordisurf aswell as
envfit to see if there were non-linearities, and, in all bar a single case,
the surface isobars happened to be straight. As it turns out, the one that
wasnt linear didnt make sense in terms of the analysis in any case.

A question perhaps more for Jari, the PCA results (eigenvalues and vectors,
but not rank) from rda seem to be slightly different in terms of scale than
other functions (e.g. labdsv:::pca which wraps stats:::prcomp i believe).
Plots look identical, except for the size of the axis scores - rda scores
seem to be approximately 1/5th of pca scores. Why is that? Is there a
solution to rescale them?


Thanks again,

Alan


--------------------------------------------------
Email: aghaynes@gmail.com
Mobile: +41794385586
Skype: aghaynes


On 10 May 2012 14:57, Martin Weiser <weiser2@natur.cuni.cz> wrote:

> Hi Alan,
> I think that PCA is even better with envfit than NMDS with envfit. This
> is because PCA works in linear euclidean world, so correlation makes
> better sense in this case. You are correlating points on lines (envfit)
> with points on lines (PCA), rather than points on lines (envfit) with
> undetermined something non-regularly stressed (NMDS).
> But this is just my feeling, I may be wrong easily, but in that case I
> hope someone will correct me.
> Best,
> Martin Weiser
>
> Alan Haynes píše v Čt 10. 05. 2012 v 13:17 +0200:
> > Hi all,
> >
> > Im using envfit with some decomposition data currently but with a PCA
> > result (via vegan:::rda()). Is envfit still valid for PCA results? I
> guess
> > it doesnt make so very much difference, just the interpretation is
> slightly
> > different.
> > Or am I barking up the wrong tree by using this approach?
> >
> > Cheers,
> >
> > Alan
> >
> > --------------------------------------------------
> > Email: aghaynes@gmail.com
> > Mobile: +41794385586
> > Skype: aghaynes
> >
> >
> > On 10 May 2012 12:53, Gavin Simpson <gavin.simpson@ucl.ac.uk> wrote:
> >
> > > I've removed R-Help from this now...
> > >
> > > On Thu, 2012-05-10 at 10:13 +0000, Jari Oksanen wrote:
> > > > On 10/05/2012, at 11:45 AM, Gavin Simpson wrote:
> > > <snip />
> > > > > As you provide little or no context I'll explain what envfit() does
> > > etc.
> > > > >
> > > > > The idea goes back a long way (!) and is in my 1995 edition of
> Jongman
> > > > > et al Data Analysis in Community and Landscape Ecology (Cambridge
> > > > > University Press) though most likely was in 1987 version too. See
> > > > > Section 5.4 of the Ordination chapter by Ter Braak in that book.
> > > > >
> > > > > The idea is to find the direction (in the k-dimensional ordination
> > > > > space) that has maximal correlation with an external variable.
> > > >
> > > >
> > > > Hello,
> > >
> > > <snip />
> > >
> > > > Then about Bray-Curtis. The referee may be correct when writing that
> > > > the fitted vectors are not directly related to Bray-Curtis. You fit
> > > > the vectors to the NMDS ordination, and that is a non-linear mapping
> > > > from Bray-Curtis to the metric ordination space.  There are two
> points
> > > > here: non-linearity and stress. Because of these, it is not strictly
> > > > about B-C. Of course, the referee is wrong when writing about NMDS
> > > > axes: the fitted vector has nothing to do with axes (unless you
> rotate
> > > > your axis parallel to the fitted vector which you can do). The NMDS
> is
> > > > based on Bray-Curtis, but it is not the same, and the vector fitting
> > > > is based on NMDS. So why not write that is about NMDS? Why to insist
> > > > on Bray-Curtis which is only in the background?
> > >
> > > Right, agreed. The analysis is one step removed from the B-C but the
> > > point of doing the nMDS was to find a low-d mapping of these B-C
> > > distances so in the sense that *if* the mapping is a good one then we
> > > can talk about correlations between "distances" between sites and the
> > > environmental variables. Whilst it might be strictly more correct to
> > > talk about this from the point of view of the nMDS the implication is
> > > that for significant envfit()s there is a significant linear
> correlation
> > > between the environmental variable(s) and the approximate ranked
> > > distances between samples.
> > >
> > > I mean, if all we talk about is the nMDS who cares? it is the
> > > implications of this for the system under study that are of interest.
> > >
> > > That said, B-C is just one of many ways to think of distance so to my
> > > mind I wouldn't even talk about the B-C distance either; the interest
> is
> > > in differences between sites/samples. The relevance of B-C or some
> other
> > > coefficient only comes in when considering if they are a good
> descriptor
> > > of the "distance" between samples for the variables you are
> considering.
> > >
> > > Cheers,
> > >
> > > G
> > >
> > > > Cheers, Jari Oksanen
> > > >
> > >
> > > --
> > > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
> > >  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
> > >  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
> > >  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
> > >  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
> > >  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
> > > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
> > >
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