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    The Use of Regression for Detecting Competition with Multicollinear Data

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    Slade_1988.pdf (16.32Mb)
    Issue Date
    1988-07-01
    Author
    Carnes, Bruce A.
    Slade, Norman A.
    Publisher
    Ecological Society of America
    Type
    Article
    Article Version
    Scholarly/refereed, publisher version
    Rights
    Copyright by the Ecological Society of America.
    Metadata
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    Abstract
    Monte Carlo simulations were used to demonstrate that regression methods could be successfully used to estimate competition coefficients with collinear data, but only under conditions that may be difficult to meet with ecological data. Ordinary least squares performs well when estimating coefficients associated with noncollinear predictor variables. Stepwise regression and maximum eigenvalue least squares reduce collinearity by deleting information that, even though not statistically significant, may be important in accurately estimating interaction. We propose that apparent competition (Holt 1977) and the failure of regression techniques to detect competition when it is known to exist experimentally may be due to the omission or lack of measurement of critical elements of the community matrix.
    URI
    http://hdl.handle.net/1808/17938
    DOI
    https://doi.org/10.2307/1941282
    Collections
    • Ecology and Evolutionary Biology Scholarly Works [1386]
    Citation
    Carnes, Bruce A.; Slade, Norman A. "The use of regression for detecting competition with multicollinear data." Ecology, 69(4):1266-1274. http://dx.doi.org/10.2307/1941282.

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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