[R-sig-eco] multiple regression

[Peter Solymos]

I meant "Species richness is discrete", not categorical.
Peter

On Sat, Feb 6, 2010 at 12:52 PM, Peter Solymos <solymos at ualberta.ca> wrote:
> 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
>
> Péter Sólymos
> 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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>>
>