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GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data

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  • Silverio Vílchez-López
  • Antonio José Sáez-Castillo
  • María José Olmo-Jiménez

Abstract

Understanding why a random variable is actually random has been in the core of Statistics from its beginnings. The generalized Waring regression model for count data explains that inherent variability is given by three possible sources: randomness, liability and proneness. The model extends the negative binomial regression model and it is not included in the family of generalized linear models. In order to avoid that shortcoming, we developed the GWRM R package for fitting, describing and validating the model. The version we introduce in this communication provides a new design of the modelling function as well as new methods operating on the associated fitted model objects, so that the new software integrates easily into the computational toolbox for modelling count data in R. The release of a plug-in in order to use the package from the interface R Commander tries to contribute to the spreading of the model among non-advanced users. We illustrate the usage and the possibilities of the software with two examples from the fields of health and sport.

Suggested Citation

  • Silverio Vílchez-López & Antonio José Sáez-Castillo & María José Olmo-Jiménez, 2016. "GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:plo:pone00:0167570
    DOI: 10.1371/journal.pone.0167570
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    Cited by:

    1. Gning, Lucien & Ndour, Cheikh & Tchuenche, J.M., 2022. "Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).

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