Regression Models for Count Data in R

Simon Jackman, Christian Kleiber and Achim Zeileis ()

Working papers from Faculty of Business and Economics - University of Basel

Abstract: The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of zero-inflated and hurdle regression models in the functions zeroinfl() and hurdle() from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both model classes are able to incorporate over-dispersion and excess zeros - two problems that typically occur in count data sets in economics and the social and political sciences - better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be fitted, inspected and tested in practice.

Keywords: GLM; /; Poisson; model; /; negative; binomial; model; /; zero-inflated; model; /; hurdle; model; URL (search for similar items in EconPapers)
Date: 2007-12-20
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Citations: View citations in EconPapers (6)

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Journal Article: Regression Models for Count Data in R (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:bsl:wpaper:2007/24

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