[R-sig-eco] multiple regression

[Aitor Gastón]

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 at gmail.com>
Sent: Saturday, February 06, 2010 5:17 PM
To: <r-sig-ecology at 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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