overdisp: Overdispersion in Count Data Multiple Regression Analysis

Detection of overdispersion in count data for multiple regression analysis. Log-linear count data regression is one of the most popular techniques for predictive modeling where there is a non-negative discrete quantitative dependent variable. In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language.

Getting started

Package details

AuthorRafael Freitas Souza [cre], Luiz Paulo Favero [ctb], Patricia Belfiore [ctb], Hamilton Luiz Correa [ctb], A. Colin Cameron [aut], Pravin Trivedi [aut]
MaintainerRafael Freitas Souza <fsrafael@usp.br>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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overdisp documentation built on March 26, 2020, 9:14 p.m.