Ordinal response regression models in ecology

Ordinal response regression models in ecology Abstract. Although ordinal data are not rare in ecology, ecological studies have, until now, seriously neglected the use of specific ordinal regression models. Here, we present three models – the Proportional Odds, the Continuation Ratio and the Stereotype models – that can be successfully applied to ordinal data. Their differences and respective fields of application are discussed. Finally, as an example of application, PO models are used to predict spatial abundance of plant species in a Geographical Information System. It shows that ordinal models give as good a result as binary logistic models for predicting presence‐absence, but are additionally able to predict abundance satisfactorily. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Vegetation Science Wiley

Ordinal response regression models in ecology

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Publisher
Wiley
Copyright
2000 IAVS ‐ the International Association of Vegetation Science
ISSN
1100-9233
eISSN
1654-1103
DOI
10.2307/3236568
Publisher site
See Article on Publisher Site

Abstract

Abstract. Although ordinal data are not rare in ecology, ecological studies have, until now, seriously neglected the use of specific ordinal regression models. Here, we present three models – the Proportional Odds, the Continuation Ratio and the Stereotype models – that can be successfully applied to ordinal data. Their differences and respective fields of application are discussed. Finally, as an example of application, PO models are used to predict spatial abundance of plant species in a Geographical Information System. It shows that ordinal models give as good a result as binary logistic models for predicting presence‐absence, but are additionally able to predict abundance satisfactorily.

Journal

Journal of Vegetation ScienceWiley

Published: Oct 1, 2000

References

  • An application of logistic models to the analysis of ordinal responses
    Greenland, Greenland
  • Alternative models for ordinal logistic regression
    Greenland, Greenland
  • GLM versus CCA spatial modeling of plant species distribution
    Guisan, Guisan; Weiss, Weiss; Weiss, Weiss
  • Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors
    Harrell, Harrell; Lee, Lee; Mark, Mark

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