We introduce the R package openEBGM, an implementation of the Gamma-Poisson Shrinker (GPS) model for identifying unexpected counts in large contingency tables using an empirical Bayes approach. The Empirical Bayes Geometric Mean (EBGM) and quantile scores are obtained from the GPS model estimates. openEBGM provides for the evaluation of counts using a number of different methods, including the model-based disproportionality scores, the relative reporting ratio (RR), and the proportional reporting ratio (PRR). Data squashing for computational efficiency and stratification for confounding variable adjustment are included. Application to adverse event detection is discussed.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-063.zip
openEBGM, PhViD, mederrRank, tidyr, ggplot2, data.table
Bayesian, Finance, Graphics, HighPerformanceComputing, Phylogenetics
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For attribution, please cite this work as
Canida & Ihrie, "The R Journal: openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model", The R Journal, 2017
BibTeX citation
@article{RJ-2017-063,
author = {Canida, Travis and Ihrie, John},
title = {The R Journal: openEBGM: An R Implementation of the Gamma-Poisson Shrinker Data Mining Model},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-063},
doi = {10.32614/RJ-2017-063},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {499-519}
}