Nathan,  r-sig-ecology folks,
 
Stepwise variable selection certainly has its shortcomings but I would point out the following paper: 
 
Murtaugh, P.A. 2009. Performance of several variable-selection methods applied to real ecological data. Ecology Letters 12:1061-1068.
 
Essentially, all methods of variable selection (stepwise, AIC, BIC, regression trees, etc) performed similarly.  So, if there is a signal in your data, you are likely to detect it using a variety of methods.
 
Kind Regards,
Joe
 
Joe Fontaine
Postdoctoral Researcher
Environmental Science
90 South St
Murdoch University
Perth, Australia 6150
J.Fontaine@murdoch.edu.au
 

________________________________

From: r-sig-ecology-bounces@r-project.org on behalf of Aitor Gastón
Sent: Tue 2/9/2010 2:19 AM
To: Nathan Lemoine; r-sig-ecology@r-project.org
Subject: Re: [R-sig-eco] multiple regression




Hi Nathan,

Many authors criticize stepwise variable selection, e.g., Harrell, F.E.,
2001, Regression modelling strategies with applications to linear models,
logistic regression and survival analysis.  You can find some of his
arguments and extra references in
http://childrens-mercy.org/stats/faq/faq12.asp

Cheers,

Aitor

--------------------------------------------------
From: "Nathan Lemoine" <lemoine.nathan@gmail.com>
Sent: Saturday, February 06, 2010 5:17 PM
To: <r-sig-ecology@r-project.org>
Subject: [R-sig-eco] multiple regression

> Hi everyone,
>
> I'm trying to fit a multiple regression model and have run into some
> questions regarding the appropriate procedure to use. I am trying to
> compare fish assemblages (species richness, total abundance, etc.) to
> metrics of habitat quality. I swam transects are recorded all fish
> observed, then I measured the structural complexity and live coral  cover
> over each transect. I am interested in weighting which of these  two
> metrics has the largest influence on structuring fish assemblages.
>
> My strategy was to use a multiple linear regression. Since the data  were
> in two different measurement units, I scaled the variables to a  mean of 0
> and std. dev. of 1. This should allow me to compare the  sizes of the beta
> coefficients to determine the relative (but not  absolute) importance of
> each habitat variable on the fish assemblage,  correct?
>
> My model was lm(Species Richness~Complexity+Coral Cover). I had run a
> full model and found no evidence of interactions, so I ran it without  the
> interaction present.
>
> It turns out coral cover was not significant in any regression. I have
> been told that the test I used was incorrect and that the appropriate
> procedure is a stepwise regression, which would, undoubtedly, provide  me
> with Complexity as a significant variable and remove Coral Cover.  This
> seems to me to be the exact same interpretation as the above  model. So,
> since I'm very new to all of this, I am wondering how to  tell whether one
> model is 'incorrect' or 'inappropriate' given that  they yield almost
> identical results? What are the advantages of a  stepwise regression over
> a standard multiple regression like I have run?
>
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>

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