Title: GLMcat: An R package for generalized linear models for categorical responses
Authors: Lorena Leon - Universite de Montpellier, CIRAD (France) [presenting]
Jean Peyhardi - University of Montpellier (France)
Catherine Trottier - University of Montpellier (France)
Abstract: In statistical modeling, there is a wide variety of regression models for categorical responses. Yet, no software encapsulates all of these models in a standardized format. We introduce and illustrate the utility of GLMcat, the R package we developed to estimate generalized linear models implemented under the unified specification $(r, F, Z)$, where $r$ represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), $F$ the cumulative distribution function for the linkage, and $Z$ the design matrix. We present the properties of the four families of models, which must be investigated when selecting the components $r$, $F$, and $Z$. The functions are user-friendly and fairly intuitive; offering the possibility to choose from a large range of models through a combination $(r, F, Z)$. Through different examples, we compare our package with VGAM and ordinal, two popular packages for implementing GLMs for categorical data.