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GLM and GAM for Absence–Presence and Proportional Data

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

Abstract

In the previous chapter, count data with no upper limit were analysed using Poisson generalised linear modelling (GLM) and negative binomial GLM. In Section 10.2 of this chapter, we discuss GLMs for 0−1 data, also called absence–presence or binary data, and in Section 10.3 GLM for proportional data are presented. In the final section, generalised additive modelling (GAM) for these types of data is introduced. A GLM for 0−1 data, or proportional data, is also called logistic regression.

Keywords

  • Generalise Linear Modelling
  • Wild Boar
  • Linear Regression Model
  • Generalise Additive Modelling
  • Predictor Function

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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Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M. (2009). GLM and GAM for Absence–Presence and Proportional Data. 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_10

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