R中的三向对应分析

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2023-11-09 DOI:10.32614/rj-2023-049
Rosaria Lombardo, Michel van de Velden, Eric J. Beh
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引用次数: 0

摘要

三向对应分析是一种适用于三向分类数据关联可视化、全局依赖性建模或降维的多变量分析方法。本文提供了一个用于执行三向通信分析的R包的描述:ca3变体。此包中的功能允许分析人员根据所提出的研究问题和/或数据背后的属性执行此分析的几种变体。用户可以选择经典(对称)方法或非对称变体——如果将三个分类变量中的一个作为响应变量,后者特别有用。此外,为了执行必要的三向分解,可以使用Tucker3和三元矩分解(使用正交多项式)。当一个或多个分类变量是名义变量时,可以使用Tucker3分解方法,而对于有序变量,可以使用三元矩分解方法。该包还提供了一个可用于选择模型维度的函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Three-Way Correspondence Analysis in R
Three-way correspondence analysis is a suitable multivariate method for visualising the association in three-way categorical data, modelling the global dependence, or reducing dimensionality. This paper provides a description of an R package for performing three-way correspondence analysis: CA3variants. The functions in this package allow the analyst to perform several variations of this analysis, depending on the research question being posed and/or the properties underlying the data. Users can opt for the classical (symmetrical) approach or the non-symmetric variant - the latter is particularly useful if one of the three categorical variables is treated as a response variable. In addition, to perform the necessary three-way decompositions, a Tucker3 and a trivariate moment decomposition (using orthogonal polynomials) can be utilized. The Tucker3 method of decomposition can be used when one or more of the categorical variables is nominal while for ordinal variables the trivariate moment decomposition can be used. The package also provides a function that can be used to choose the model dimensionality.
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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