Visualizing Class Specific Heterogeneous Tendencies using R

📅 2026-09-03
📈 Citations: 0
Influential: 0
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🤖 AI Summary
本文介绍了一种使用R包mccca实现的方法MCCCA,用于识别并可视化不同类别(如性别、国籍)特有的异质性趋势。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

MCCCA
heterogeneous tendencies
visualization
class-specific
Innovation

Methods, ideas, or system contributions that make the work stand out.

MCCCA
heterogeneous tendencies
external variables
active variables
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Mariko Takagishi
Faculty of Engineering, Mathematical and Data Sciences Program, Graduate School of Environmental and Life Science, Okayama University
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Michel van de Velden
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam