Regression models for count data in R

Achim Zeileis

doi:10.18637/jss.v027.i08

Regression models for count data in R

2008-01-01

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DOI：10.18637/jss.v027.i08

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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-in 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.

Keywords:

GLM

Poisson model

negative binomial model

hurdle model

zero-inflated model

Regression models for count data in R

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