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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5148598?pdf=render |
| _version_ | 1811269909718499328 |
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| author | Silverio Vílchez-López Antonio José Sáez-Castillo María José Olmo-Jiménez |
| author_facet | Silverio Vílchez-López Antonio José Sáez-Castillo María José Olmo-Jiménez |
| author_sort | Silverio Vílchez-López |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T21:50:29Z |
| format | Article |
| id | doaj.art-22048fbc36414d14959bbb4cc07d5b46 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T21:50:29Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-22048fbc36414d14959bbb4cc07d5b462022-12-22T03:15:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-011112e016757010.1371/journal.pone.0167570GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data.Silverio Vílchez-LópezAntonio José Sáez-CastilloMaría José Olmo-JiménezUnderstanding 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.http://europepmc.org/articles/PMC5148598?pdf=render |
| spellingShingle | Silverio Vílchez-López Antonio José Sáez-Castillo María José Olmo-Jiménez GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. PLoS ONE |
| title | GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. |
| title_full | GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. |
| title_fullStr | GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. |
| title_full_unstemmed | GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. |
| title_short | GWRM: An R Package for Identifying Sources of Variation in Overdispersed Count Data. |
| title_sort | gwrm an r package for identifying sources of variation in overdispersed count data |
| url | http://europepmc.org/articles/PMC5148598?pdf=render |
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