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.
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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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DOI: https://doi.org/10.1007/978-0-387-87458-6_10
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Publisher Name: Springer, New York, NY
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Online ISBN: 978-0-387-87458-6
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