[R-sig-eco] Why is collinearity in ordination a problem?

[Highland Statistics Ltd.]

> Date: Fri, 20 Nov 2009 12:56:28 -0000
> From: "Victoria June Ord" <v.j.ord at newcastle.ac.uk>
> Subject: [R-sig-eco] Why is collinearity in ordination a problem?
> To: <r-sig-ecology at r-project.org>
> Message-ID:
> 	<86406CC946606A4F91EAA53CF17C14CB011D78A8 at quarrel.campus.ncl.ac.uk>
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>
> Dear All
>  
> Looking at examples from Alain Zuur's 'Analysing Ecological Data'- it is apparent that collinearity is considered an issue in ordination.  Could someone explain why that is?
>   
It depends what you do. Collinearity means that covariates are related 
to each other; e.g. length and weight...or distance to the coast and 
depth (in most systems). These are all 2-way relationships....but it can 
also be at a higher dimension. And yes..it can also involve continuous 
and categorical (e.g. location in an estuary and salinity). It is 
perfectly legitimate to apply for example PCA on a set of highly 
collinear covariates. But you are in trouble if you apply one of these 
constraint ordination techniques, apply model selection techniques and 
come up with p-values.

For an example of collinearity in regression, see:

http://methodsblog.wordpress.com/2009/11/13/first-paper-now-online/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+wordpress%2Fmethodsblog+(methods.blog)

In GLMs collinearity enters itself in a slightly different way due to 
the underlying maths. And the same holds for RDA/CCA type techniques.

In my opinion collinearity is one of the most challenging problems with 
data analyses.

Alain

 


Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


Other books: http://www.highstat.com/books.htm


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