multiple regression
Peter, I would think that the species richness is binomial distributed. Since there is a maximum number of species that can be present. Therefore I would model it like glm(cbind(number.present, number.absent) ~ covariates, family = binomial) HTH, Thierry ---------------------------------------------------------------------------- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-sig-ecology-bounces at r-project.org [mailto:r-sig-ecology-bounces at r-project.org] Namens Peter Solymos Verzonden: zaterdag 6 februari 2010 20:53 Aan: Nathan Lemoine CC: r-sig-ecology at r-project.org Onderwerp: Re: [R-sig-eco] multiple regression Nathan, Species richness is categorical, so if your richness values are usually low (say < 20), you should consider the use of Poisson GLM, or log-transform your response (and log is the canonical link function for Poisson GLM). This usually improves the model fit. And this might apply to abundance as well. If you use lm(), you can inspect the residual variance of the models after excluding one of the covariates. The increase in residual variance compared to the full model will tell which variance component is higher (explains more of your data). Or you may as well inspect the anova() table of the fitted model (both for lm or glm). Best, Peter P?ter S?lymos Alberta Biodiversity Monitoring Institute Department of Biological Sciences CW 405, Biological Sciences Bldg University of Alberta Edmonton, Alberta, T6G 2E9, Canada Phone: 780.492.8534 Fax: 780.492.7635
On Sat, Feb 6, 2010 at 9:17 AM, Nathan Lemoine <lemoine.nathan at gmail.com> wrote:
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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