The VGAM package for negative binomial regression

Negative binomial (NB) regression is the most common full‐likelihood method for analysing count data exhibiting overdispersion with respect to the Poisson distribution. Usually most practitioners are content to fit one of two NB variants, however other important variants exist. It is demonstrated he...

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Bibliographic Details
Published in:Australian & New Zealand journal of statistics 2020-03, Vol.62 (1), p.116-131
Main Author: Yee, Thomas W.
Format: Article
Language:English
Subjects:
Goodness of fit
overdispersion
Poisson distribution
Regression analysis
rootogram
Statistical analysis
vector generalised additive models
vector generalised linear models
zero alteration
zero inflation
zero truncation
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Summary:Negative binomial (NB) regression is the most common full‐likelihood method for analysing count data exhibiting overdispersion with respect to the Poisson distribution. Usually most practitioners are content to fit one of two NB variants, however other important variants exist. It is demonstrated here that the VGAMR package can fit them all under a common statistical framework founded upon a generalised linear and additive model approach. Additionally, other modifications such as zero‐altered (hurdle), zero‐truncated and zero‐inflated NB distributions are naturally handled. Rootograms are also available for graphically checking the goodness of fit. Two data sets and some recently added features of the VGAM package are used here for illustration. A software tutorial of the VGAM and VGAMdata R packages of several important negative binomial variants that all practitioners should know.
ISSN:1369-1473
1467-842X
DOI:10.1111/anzs.12283