[R-sig-eco] multiple regression

[Gustaf Granath]

Standardized coefficients are not necessarily a bad idea.

Gelman A., 2008. Scaling regression inputs by dividing by two standard 
deviations. Stat Med. 2008, 27:2865-73.

GG
> ...and you can also read in Frank Harrell's book why standardized
> coefficients are a bad idea.  There is a large statistical literature
> on variable importance in regression models.  For a discussion and
> accompanying R package see
>
> @article{gr?mping2006relative,
>   title={{Relative importance for linear regression in R: the package
> relaimpo}},
>   author={Gr{\\"o}mping, U.},
>   journal={Journal of Statistical Software},
>   volume={17},
>   number={1},
>   pages={139--147},
>   year={2006},
>   publisher={American Statistical Association}
> }
>
>
> hth,
>
> Kingsford Jones
>
> 2010/2/8 Aitor Gast?n <aitor.gaston at upm.es>:
>   
>> >
>> > 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?
>>> >>
>>> >> _______________________________________________
>>> >> R-sig-ecology mailing list
>>> >> R-sig-ecology at r-project.org
>>> >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>>> >>
>>>       
>> >
>> > _______________________________________________
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>> >
>>