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Study of variable selection in multivariate methods without external variables and development of variable selection software

无外变量多元方法变量选择研究及变量选择软件开发

基本信息

批准号：
14580352
负责人：
MORI Yuichi
金额：
$ 1.92万
依托单位：
Okayama University of Science
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

项目摘要

We study variable selection in multivariate methods without external variables such as principal component analysis (PCA), factor analysis (FA) and correspondence analysis (CA). In this study we discuss the followings.1.Existing methods of variable selection in multivariate methods without external variables are collected from literatures. They are reviewed and then published as overviews of variable selection in our papers and on the web (see 4)2.New selection criteria in PCA, FA and CA are proposed. In PCA modified principal component as a selection criterion is evaluated, information on how many variables should be used is discussed using computer-intensive methods, and a selection criterion in the sense of regression is proposed and evaluated by AIC. In FA, criteria using RV-coefficient to evaluate the closeness between a configuration of factor scores based on all variables and one on selected variables is considered. In CA, a selection criterion using RV-coefficient for the closeness on two case scores and criteria using ordinary goodness of fit in CA are discussed.3.Applying the proposed criteria to real data sets, they are found that every criterion can be used in the real situations and that selection procedures such as forward-backward stepwise selection provide similar results to all possible selection.4.Variable selection environment"VASMM"(VAriable Selection in Multivariate Methods) is established on the web (http://mo161.soci.ous.ae.jp/vasmm/) to provide information on variable selection in multivariate methods without external variables and on-line selection function. Macros for general statistical packages such as R and XploRe are developed. Using these tools variable selection can be performed anytime and anywhere.

我们研究无外部变量的多变量方法中的变量选择，例如主成分分析（PCA）、因子分析（FA）和对应分析（CA）。在本研究中我们讨论以下内容： 1.无外部变量的多元方法中现有的变量选择方法是从文献中收集的。它们经过审查，然后作为变量选择的概述发布在我们的论文和网络上（参见 4）2。提出了 PCA、FA 和 CA 中的新选择标准。在 PCA 修改主成分作为选择标准进行评估时，使用计算机密集方法讨论应使用多少变量的信息，并提出回归意义上的选择标准并通过 AIC 进行评估。在 FA 中，考虑使用 RV 系数来评估基于所有变量的因子得分配置与基于选定变量的因子得分配置之间的接近度的标准。在CA中，讨论了使用RV系数表示两个案例得分接近度的选择标准和CA中使用普通拟合优度的标准。3.将所提出的标准应用于实际数据集，发现每个标准都可以在实际情况中使用，并且诸如向前向后逐步选择之类的选择过程为所有可能的选择提供了相似的结果。4.变量选择环境“VASMM”（多元方法中的变量选择）建立在网络上（http://mo161.soci.ous.ae.jp/vasmm/），提供多变量方法中变量选择的信息，无需外部变量和在线选择功能。开发了用于 R 和 XploRe 等通用统计包的宏。使用这些工具可以随时随地进行变量选择。