Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Regression Models for Count Data in R | Zeileis | Journal of Statistical Software
Authors: Achim Zeileis, Christian Kleiber, Simon Jackman
Title: Regression Models for Count Data in R
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 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.

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Paper: Regression Models for Count Data in R     Download PDF (Downloads: 32460)
pscl_1.00.tar.gz: R source package Download (Downloads: 3230; 627KB) v27i08.R: R example code from the paper Download (Downloads: 3716; 2KB) DebTrivedi.rda: Example data from Deb & Trivedi (1997) in R binary format Download (Downloads: 3790; 46KB)

DOI: 10.18637/jss.v027.i08

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.