可视化分类数据中的类特定异质趋势
Visualizing class specific heterogeneous tendencies in categorical data
摘要：
在多重对应分析中，个体（观察）和类别都可以用双标图表示，双标图共同描绘了类别或个体之间的关系，以及它们之间的关联。关于个体的额外信息可以增强解释能力，例如通过包括分类变量，其相互依赖性不是直接关注的，但有助于解释关于个体和类别之间的关系的图。本文提出了一种新的方法来添加这样的信息，根据多集聚类对应分析方法，识别特定于类的聚类，或对应于附加变量类别的数据子集。所提出的方法可以构建一个双标图，描绘个体成员的异质性趋势，以及它们与原始分类变量的关系。模拟研究，调查所提出的方法的性能和应用程序的数据，在英国的道路交通事故证实了这种方法的可行性。
英文摘要：
In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories or individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including categorical variables 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 for adding such information, according to a multiple-set cluster correspondence analysis approach that identifies clusters specific to classes, or subsets of the data that correspond to the categories of the additional variables. 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 this proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
