Regression Models for Count Data in R
Stanford University · University of Basel · +1 more institution
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…
Citation impact
- FWCI
- 33.74
- Percentile
- 100%
- References
- 27
Authors
3- AZAchim ZeileisCorresponding
Stanford University, University of Basel, Vienna University of Economics and Business
- CKChristian Kleiber
Stanford University, Vienna University of Economics and Business, University of Basel
- SJSimon Jackman
Stanford University, Vienna University of Economics and Business, University of Basel
Topics & keywords
- Count data
- Negative binomial distribution
- Code (set theory)
- Toolbox
- Computer science
- Overdispersion
- Quasi-likelihood
- Poisson regression