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
Authors: C. Mitchell Dayton
Title: SUBSET: Best Subsets using Information Criteria
Abstract: SUBSET, written in the matrix language Gauss, is a program that identifies optimal subsets of means or proportions based on independent groups. All possible configurations of ordered subsets of groups are identified and the best model is selected using either the AIC or BIC information criterion. For means, both homogeneous and heterogeneous variance cases are considered. SUBSET offers an alternative approach to traditional post-hoc multiple-comparison procedures such as the Tukey test for pairwise comparisons. Major advantages of SUBSET over traditional pairwise comparison procedures include the fact that intransitive decisions are avoided and that issues related to type I error control, sample size and heterogeneity of variance do not arise.

Page views:: 5984. Submitted: 2000-04-04. Published: 2001-03-05.
Paper: SUBSET: Best Subsets using Information Criteria     Download PDF (Downloads: 6286)
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Code.rtf: program for computing Akaike AIC and Schwarz BIC statistics for ordered subsets of means or proportions Download (Downloads: 5461; 10KB)

DOI: 10.18637/jss.v006.i02

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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.