Premium
The VGAM package for negative binomial regression
Author(s) -
Yee Thomas W.
Publication year - 2020
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12283
Subject(s) - overdispersion , negative binomial distribution , mathematics , poisson regression , zero inflated model , poisson distribution , statistics , count data , binomial (polynomial) , quasi likelihood , goodness of fit , regression , r package , regression analysis , zero (linguistics) , econometrics , medicine , population , linguistics , philosophy , environmental health
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 VGAM R 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.