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


RIS


BibTeX

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.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title Regression Models for Count Data in R
Volume 27
Number 8
Year 2008
Page from 1
Page to 25
Reviewed? Y
URL http://www.jstatsoft.org/v27/i08/
DOI http://dx.doi.org/10.18637/jss.v027.i08

Associations

People
Zeileis, Achim (Former researcher)
External
Jackman, Simon
Kleiber, Christian
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search