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Getting Started with RAn Introduction for Biologists$
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Andrew Beckerman, Dylan Childs, and Owen Petchey

Print publication date: 2017

Print ISBN-13: 9780198787839

Published to Oxford Scholarship Online: March 2017

DOI: 10.1093/acprof:oso/9780198787839.001.0001

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Getting Started with Generalized Linear Models

Getting Started with Generalized Linear Models

Chapter:
(p.167) 7 Getting Started with Generalized Linear Models
Source:
Getting Started with R
Author(s):

Andrew P. Beckerman

Dylan Z. Childs

Owen L. Petchey

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198787839.003.0007

We introduce generalized linear models (GLMs), demonstrating by comparison with a linear model when, why, and how these models can be valuable and important to the biologist. The chapter focuses on a single example about sheep, where the response variable is count data (number of offspring), to demonstrate how easy it is to work with GLMs in R (i.e. we demonstrate Poisson regression). Several conceptual/theoretical aspects of fitting generalized linear models are introduced, such as family, link, and error distribution, with graphical explanations. Following the same general workflow, we demonstrate how to assess various diagnostics and make predictions from and visualize results from these models.

Keywords:   generalized linear model, count data, family, link, error distribution, GLM, Poisson regression

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