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
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Email: aghaynes at gmail.com
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On 10 May 2012 12:53, Gavin Simpson <gavin.simpson at 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:
As you provide little or no context I'll explain what envfit() does
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.
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
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Dr. Gavin Simpson [t] +44 (0)20 7679 0522
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