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.
| Version: |
0.1.2 |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2023-07-04 |
| DOI: |
10.32614/CRAN.package.overdisp |
| Author: |
Rafael Freitas Souza [cre],
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 |
| Materials: |
README |
| CRAN checks: |
overdisp results |
Documentation:
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