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

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 nega...

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Bibliographic Details
Published in:PloS one 2016-12, Vol.11 (12), p.e0167570-e0167570
Main Authors: Vílchez-López, Silverio, Sáez-Castillo, Antonio José, Olmo-Jiménez, María José
Format: Article
Language:English
Subjects:
Athletic Performance
Computer and Information Sciences
Computer applications
Computer programs
Earth Sciences
Economic models
Engineering and Technology
Humans
Liability
Linear Models
Medicine and Health Sciences
Models, Statistical
Models, Theoretical
Normal distribution
Office Visits
Operations research
Physical Sciences
Random variables
Regression Analysis
Regression models
Research and Analysis Methods
Scientometrics
Software
Sports
Statistical models
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0167570