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Regression models for count data in R
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 103016
Author(s) Zeileis, Achim; Kleiber, Christian; Jackman, Simon
Author(s) at UniBasel Kleiber, Christian
Year 2008
Title Regression models for count data in R
Journal Journal of Statistical Software
Volume 27
Number 8
Pages / Article-Number 1-25
Keywords GLM, Poisson model, negative binomial model, hurdle model, zero-inflated model
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.

Publisher JOURNAL STATISTICAL SOFTWARE
ISSN/ISBN 1548-7660
URL http://www.jstatsoft.org/v27/i08/paper
edoc-URL http://edoc.unibas.ch/dok/A5252903
Full Text on edoc Available
Digital Object Identifier DOI 10.18637/jss.v027.i08
ISI-Number WOS:000258207100001
Document type (ISI) Article
 
   

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