[R-sig-eco] [R] reception of (Vegan) envfit analysis by manuscript reviewers

[Gavin Simpson]

On Thu, 2012-05-10 at 13:17 +0200, Alan Haynes wrote:
> 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?

It is perfectly valid and is introduced in Jongman et al alongside PCA
and CA. We (well Jari) wouldn't have written a method for objects of
class "cca" if it wasn't appropriate.

I suggest you look at ordisurf() though; in most of the projects I have
been involved in, the linearity assumption of envfit() is questionable.

If you want a bit more info on what ordisurf() is doing see my blog post
on the function: http://wp.me/pZRQ9-1x

HTH

G

> Cheers,
> 
> Alan
> 
> --------------------------------------------------
> Email: aghaynes at gmail.com
> Mobile: +41794385586
> Skype: aghaynes
> 
> 
> 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:
>         
>         <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
>         >
>         
>         --
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 Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
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