Infantosi Antonio Fernando Catelli, Costa João Carlos da Gama Dias, Almeida Renan Moritz Varnier Rodrigues de
Cad Saude Publica. 2014 Mar;30(3):473-86. doi: 10.1590/0102-311x00128513.
Categorical variables are common in the biomedical field, and many descriptive methods have been proposed for revealing intrinsic patterns in data. Correspondence Analysis is an especially useful method for categorical data analysis of large contingency tables. Although numerous studies have been published on this method, most Portuguese-language articles have failed to explore its full potential, focusing only on graphical interpretation. The current paper reviews the method, showing that graphical analysis can be enriched by the right statistics. The article presents the mathematical basis for correspondence analysis and its most frequently used statistics. The procedure has shown that such statistics enrich symmetric map evaluation, that a low relative frequency category can be represented by supplementary category points, and that inertia contributions are highly related to residual analysis of contingency tables, not easily visualized by symmetric maps. Correspondence Analysis has proven advantageous when compared to principal components analysis.
分类变量在生物医学领域很常见，并且已经提出了许多描述性方法来揭示数据中的内在模式。对应分析是一种特别有用的方法，用于对大型列联表进行分类数据分析。尽管关于此方法已经发表了大量研究，但大多数葡萄牙语文章都未能充分挖掘其潜力，仅侧重于图形解释。本文回顾了该方法，表明正确的统计数据可以丰富图形分析。文章介绍了对应分析的数学基础及其最常用的统计数据。该过程表明，此类统计数据丰富了对称图评估，低相对频率类别可以由补充类别点表示，并且惯性贡献与列联表的残差分析高度相关，而对称图不易直观显示。与主成分分析相比，对应分析已证明具有优势。
