[R-sig-eco] Using residuals as dependent variables

[Steve Brewer]

Chris,

Another thing to keep in mind is that when you run the regression analysis
using residuals, as opposed to putting all predictors in the multiple
regression from the beginning (oceanographic data and productivity data),
you are in effect inflating the error df for the analysis of catch
residuals against productivity. In the multiple regression approach, one
df is removed from the error df for every predictor variable in the model.
When you run it as two separate analyses, as you propose, the df removed
from the error df in the first analysis (the one with oceanographic data)
are are put back in into the error df for the second analysis of catch
residuals vs productivity. This is usually not a big deal when the first
analysis contains only one or two predictors and lots of observations. But
when the reverse is true, you're more likely to get a significant
relationship between catch residuals and productivity even when none
really exists.

As others have suggested, why not put productivity and oceanographic data
together in a single mult reg model?

Hope this helps.

Steve



J. Stephen Brewer 
Professor 
Department of Biology
PO Box 1848
 University of Mississippi
University, Mississippi 38677-1848
 Brewer web page - http://home.olemiss.edu/~jbrewer/
FAX - 662-915-5144
Phone - 662-915-1077




On 6/21/12 12:06 PM, "Chris Mcowen" <chrismcowen at gmail.com> wrote:

>Dear List,
>
> 
>
>I am wondering if the methodological approach I am taking is correct and
>would be very grateful if people could comment and make suggestions, much
>appreciated.
>
> 
>
>I have developed the best model ( AIC model selection) using oceanographic
>data ( i.e. SST, chlorophyll, NPP...x6) to predict reported fisheries
>catch
>for 52 countries.
>
> 
>
>I then extract the residuals from the model and anything positive has a
>higher catch than would be predicted given the level of productivity in
>the
>country, with the opposite being true.
>
> 
>
>What I want to do is:
>
> 
>
>1.       Regress a suite of ecological and socioeconomic variables against
>the residuals from the oceanographic model to determine which factors
>cause
>some countries to be above and some below. I.E as trophic level increase
>the
>residuals become increasingly negative.
>
>2.       Ideally ( and I am unsure how or if it is possible) work out for
>each country which variables/s cause the poor fit of that country to the
>oceanographic model.
>
> 
>
>Thanks in advance for any suggestions / possible methods.
>
> 
>
>Chris 
>
> 
>
>P.S - Below is the type of conclusions I am drawing
>
> 
>
>There are a number of reasons why some countries have higher / lower catch
>than you would expect.
>
> 
>
>For example if the target fishery is a high trophic level species then the
>link between primary productivity and catch will be lesser than if the
>species was a lower trophic level ( transfer efficiency etc etc)-
>resulting
>in a negative residual.  Alternatively it maybe that the area is being
>overfished i.e. the north sea meaning more fish are being caught in that
>region than it can sustain - resulting in a high positive residual (as
>predicted by the model)
>
> 
>
>In reality it is likely a combination of this plus other, however some
>factors will be relevant to others i.e. Somalia has a really low catch
>compared to its productivity likely due to piracy and poor reporting of
>statistics. 
>
> 
>
> 
>
> 
>
>
>	[[alternative HTML version deleted]]
>
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