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) 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.


Reference manual

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0.1.1 by Rafael Freitas Souza, a year ago

Browse source code at https://github.com/cran/overdisp

Authors: Rafael Freitas Souza [cre] , Luiz Paulo Favero [ctb] , Patricia Belfiore [ctb] , Hamilton Luiz Correa [ctb] , A. Colin Cameron [aut] , Pravin Trivedi [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

See at CRAN