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

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.

13 pages

References in corpus (1)

• Copula Gaussian graphical models and their application to modeling functional disability data