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.
| Version: | 0.1.0 |
| Published: | 2020-02-16 |
| Author: | Rafael Freitas Souza [cre], Luiz Paulo Favero [ctb], Patricia Belfiore [ctb], Hamilton Luiz Correa [ctb], A. Colin Cameron [aut], Pravin Trivedi [aut] |
| Maintainer: | Rafael Freitas Souza <fsrafael at usp.br> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | overdisp results |
| Reference manual: | overdisp.pdf |
| Package source: | overdisp_0.1.0.tar.gz |
| Windows binaries: | r-devel: overdisp_0.1.0.zip, r-release: overdisp_0.1.0.zip, r-oldrel: overdisp_0.1.0.zip |
| macOS binaries: | r-release: overdisp_0.1.0.tgz, r-oldrel: overdisp_0.1.0.tgz |
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