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Summary

Earlier chapters introduced modeling approaches for a continuous, normally distributed response. Biological data are often not so neat, and the common practice was to transform continuous response variables until the assumption of normality was met. Other kinds of data, particularly presence–absence and behavioral responses and counts, are discrete rather than continuous and require a different approach. In this chapter, we introduce generalized linear models and their extension to generalized linear mixed models to analyze these response variables. We show how common techniques such as contingency tables, loglinear models, and logistic and Poisson regression can be viewed as generalized linear models, using link functions to create the appropriate relationship between response and predictors. The models described in earlier chapters can be reinterpreted as a version of generalized linear models with the identity link function. We finish by introducing generalized additive models for where a linear model may be unsuitable.

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Applies the GLM framework to modeling event count data. Discusses the common problem of overdispersion and the methods for extending the model to account for it.

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13 - Generalized Linear Models for Categorical Responses

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10 - Strategies for Analyzing Count Data

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2 results

13 - Generalized Linear Models for Categorical Responses

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10 - Strategies for Analyzing Count Data

Summary