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Fuzzy Coding in Constrained Ordinations

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  • Michael Greenacre

Abstract

Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report we demonstrate the advantages of the fuzzy coding of explanatory variables: first, nonlinear relationships can be diagnosed; second, more variance in the responses can be explained; and third, in the presence of categorical explanatory variables (for example, years, regions) the interpretation of the resulting triplot ordinations is unified because all explanatory variables are measured at a categorical level.

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Bibliographic Info

Paper provided by Barcelona Graduate School of Economics in its series Working Papers with number 640.

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Date of creation: Jun 2012
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Handle: RePEc:bge:wpaper:640

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Keywords: canonical correspondence analysis; crisp coding; dummy variables; fuzzy coding; redundancy analysis;

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  1. Oleg Nenadic & Michael Greenacre, . "Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package," Journal of Statistical Software, American Statistical Association, vol. 20(i03).
  2. Michael Greenacre, 2009. "Contribution biplots," Economics Working Papers 1162, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2011.
  3. Zerrin Asan & Michael Greenacre, 2008. "Biplots of fuzzy coded data," Economics Working Papers 1077, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2011.
  4. Greenacre Michael, 2010. "Biplots in Practice," Books, Fundacion BBVA / BBVA Foundation, number 2011113.
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