发表机构
Okayama University; Erasmus University Rotterdam（冈山大学; 鹿特丹伊拉斯姆斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍R包mccca，实现多类别聚类对应分析（MCCCA），可在低维空间识别并可视化类别特有的异质趋势，通过两个数据集示例说明其应用与结果可视化方法。
AI 中文摘要
本文介绍了R包mccca，其实现了由[该链接]等人（2022）提出的多类别聚类对应分析（MCCCA）。MCCCA是一种统计方法，可在低维空间中识别并可视化“类别”（如性别、国籍）特有的异质趋势。在MCCCA中，需区分两类变量：外部变量直接定义类别，活动变量用于推导观测值（个体）的类别特定聚类。本文展示了如何应用MCCCA以及如何使用mccca可视化其结果，并通过将其应用于两个数据集来说明执行MCCCA的过程。
英文摘要
In this paper we introduce the R package mccca, which implements multiple-class cluster correspondence analysis (MCCCA) proposed in M.Takagishi et al., (2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to ``classes'' (e.g., gender and nationality) in a low dimensional space. In MCCCA, two kinds of variables, external and active variables, are distinguished. External variables directly define classes whereas the active variables are used to derive class-specific clusters of observations (individuals). In this paper, we show how to apply MCCCA and how to visualize its results using mccca. We illustrate the procedure for performing MCCCA by applying it to two data sets.
Comments17 pages, 5 figures
