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Environmental and Ecological Statistics

, Volume 18, Issue 3, pp 427–446 | Cite as

Compositional analysis of overdispersed counts using generalized estimating equations

  • David I. Warton
  • Peter Guttorp
Article

Abstract

Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions. In this paper, we propose a log-linear marginal modeling approach for analyzing such compositional count data, via generalized estimating equations. This method exploits the multiplicative nature of log-linear models for counts, by reparameterizing models that describe marginal effects on mean abundance. This allows partitioning into “main effects” and compositional effects, which is appealing for interpretation. We apply the proposed approach to reanalyze compositional counts of benthic invertebrates from Delaware Bay, and data of invertebrate communities inhabiting Acacia plants in eastern Australia. In both cases we resort to a resampling approach to make inferences about regression parameters, because the number of clusters was not large compared to cluster size.

Keywords

Bootstrap Community composition data Log-linear models Negative binomial regression Reparameterization Multivariate analysis 

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Supplementary material

10651_2010_145_MOESM1_ESM.pdf (69 kb)
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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Mathematics and Statistics, Evolution and Ecology Research CentreThe University of New South WalesSydneyAustralia
  2. 2.Department of StatisticsThe University of WashingtonSeattleUSA

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