Visualizing Class Specific Heterogeneous Tendencies in Categorical Data

Visualizing Class Specific Heterogeneous Tendencies in Categorical Data
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可视化分类数据中特定类别的异质趋势

DOI:
10.1080/10618600.2022.2035737
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发表时间:
2022
影响因子:
2.4
通讯作者:
Velden Michel van de
Velden Michel van de
中科院分区:
数学2区
文献类型:
--
作者:
Takagishi Mariko;Velden Michel van de

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在多重对应分析中,个体(观察)和类别都可以用双线图表示,共同描述类别和个体之间的关系以及它们之间的关联。关于个人的更多信息可以加强解释能力,例如,包括相互依存关系不是直接关系的类别信息,但这有助于解释关于个人和类别之间关系的情节。本文提出了一种识别特定于类的簇的新方法,称为多类簇对应分析。该方法可以构建一个描述个体成员的异质倾向以及它们与原始分类变量之间的关系的二重图。对所提出的方法的性能进行的模拟研究以及对英国道路事故数据的应用证实了该方法的可行性。这篇文章的补充材料可以在网上找到。
In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories and individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including class information for which the interdependencies are not of immediate concern, but that facilitate the interpretation of the plot with respect to relationships between individuals and categories. This article proposes a new method which we call multiple-class cluster correspondence analysis that identifies clusters specific to classes. The proposed method can construct a biplot that depicts heterogeneous tendencies of individual members, as well as their relationships with the original categorical variables. A simulation study to investigate the performance of the proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach. Supplementary materials for this article are available online.
多重对应分析中的等式约束。
DOI: 10.1207/s15327906mbr2704_4
发表时间: 1992
影响因子: 3.8
作者:
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