Regression Models for Count Data in R

Zeileis, Achim and Kleiber, Christian and Jackman, Simon (2008) Regression Models for Count Data in R. Journal of Statistical Software, 27 (8). pp. 1-25. ISSN 1548-7660

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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 hurdle and zero-inflated regression models in the functions hurdle() and zeroinfl() 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 hurdle and zero-inflated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social 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. (authors' abstract)

Item Type: Article
Additional Information: Article contains supplementary files. See
Keywords: GLM / Poisson model / negative binomial model / hurdle model / zero-inflated model
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Variance from Published Version: None
Depositing User: Elena Simukovic
Date Deposited: 14 Apr 2016 09:41
Last Modified: 19 Apr 2016 10:57
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