...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{grömping2006relative, 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 Gastón <aitor.gas...@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.nat...@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? >> >> _______________________________________________ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology