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arXiv · 1611.07505

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

Matthew Friedlander
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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.

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Matthew Friedlander. 2016-11-22. Fitting log-linear models in sparse contingency tables using the eMLEloglin R package. https://arxiv.org/abs/1611.07505

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