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Introduction

Part of the Statistics for Biology and Health book series (SBH)

Abstract

Does your data have repeated measurements; is it nested (hierarchical)? Is it sampled at multiple locations or sampled repeatedly over time? Or is your response variable heterogeneous? Welcome to our world, the world of mixed effects modelling. The bad news is that it is a complicated world. Nonetheless, it is one that few ecologists can avoid, even though it is one of the most difficult fields in statistics. Many textbooks describe mixed effects modelling and extensions, but most are highly mathematical, and few focus on ecology.

Keywords

  • Generalise Linear Modelling
  • Mixed Effect Modelling
  • Generalise Additive Modelling
  • Generalise Little Square
  • Nest Data

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Alain F. Zuur .

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© 2009 Springer Science+Business Media, LLC

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Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M. (2009). Introduction. In: Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-87458-6_1

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