Fitting log-linear models in sparse contingency tables using the eMLEloglin R package

TL;DR

This paper introduces the eMLEloglin R package, which aids in fitting log-linear models to sparse contingency tables by identifying the face of the convex support, improving inference and model selection.

Contribution

The paper presents the eMLEloglin package that determines the face of the convex support in sparse tables and integrates with glm for accurate model fitting.

Findings

Enhanced model fitting for sparse tables

Improved inference and model selection

Facilitates analysis on proper faces of convex support

Abstract

Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, and the data falls on a proper face $F$ of the convex support, there are consequences on model inference and model selection. Knowledge of the cells determining $F$ is crucial to mitigating these effects. We introduce the R package (R Core Team (2016)) eMLEloglin for determining $F$ and passing that information on to the glm package to fit the model properly.