Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: David Zamar, Brad McNeney, Jinko Graham
Title: elrm: Software Implementing Exact-Like Inference for Logistic Regression Models
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.

Page views:: 7808. Submitted: 2007-01-15. Published: 2007-10-08.
Paper: elrm: Software Implementing Exact-Like Inference for Logistic Regression Models     Download PDF (Downloads: 8631)
Supplements:
elrm_1.1.1.tar.gz: R source package Download (Downloads: 1612; 377KB)
v21i03.R.zip: v21i03.R: R example code from the paper Download (Downloads: 1567; 516B)
DOI: 10.18637/jss.v021.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.