%0 Journal Article
%T Regression Models for Count Data in R
%A Achim Zeileis
%A Christian Kleiber
%A Simon Jackman
%J Journal of Statistical Software
%D 2008
%I University of California, Los Angeles
%X 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-in ated 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 ated 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¡ªbetter 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  tted, inspected and tested in practice.
%K GLM
%K Poisson model
%K negative binomial model
%K hurdle model
%K zero-in ated model
%U http://www.jstatsoft.org/v27/i08/paper