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elrm: Software Implementing Exact-Like Inference for Logistic Regression Models

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  • Zamar, David
  • McNeney, Brad
  • Graham, Jinko

Abstract

Exact inference is based on the conditional distribution of the sufficient statistics for the parameters of interest given the observed values for the remaining sufficient statistics. Exact inference for logistic regression can be problematic when data sets are large and the support of the conditional distribution cannot be represented in memory. Additionally, these methods are not widely implemented except in commercial software packages such as LogXact and SAS. Therefore, we have developed elrm, software for R implementing (approximate) exact inference for binomial regression models from large data sets. We provide a description of the underlying statistical methods and illustrate the use of elrm with examples. We also evaluate elrm by comparing results with those obtained using other methods.

Suggested Citation

  • Zamar, David & McNeney, Brad & Graham, Jinko, 2007. "elrm: Software Implementing Exact-Like Inference for Logistic Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i03).
    • Handle: RePEc:jss:jstsof:v:021:i03
    DOI: http://hdl.handle.net/10.18637/jss.v021.i03
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    Cited by:

    1. Louis‐Paul Rivest & Serigne Abib Gaye, 2023. "Using Survey Sampling Algorithms For Exact Inference in Logistic Regression," International Statistical Review, International Statistical Institute, vol. 91(1), pages 18-34, April.

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