Rovira i Virgili University, Tarragona, Spain.
Br J Math Stat Psychol. 2009 Nov;62(Pt 3):583-600. doi: 10.1348/000711008X368295. Epub 2009 Jan 20.
Correspondence analysis (CA) is a popular method that can be used to analyse relationships between categorical variables. It is closely related to several popular multivariate analysis methods such as canonical correlation analysis and principal component analysis. Like principal component analysis, CA solutions can be rotated orthogonally as well as obliquely into a simple structure without affecting the total amount of explained inertia. However, some specific aspects of CA prevent standard rotation procedures from being applied in a straightforward fashion. In particular, the role played by weights assigned to points and dimensions and the duality of CA solutions are unique to CA. For orthogonal simple structure rotation, procedures recently have been proposed. In this paper, we construct oblique rotation methods for CA that take into account these specific difficulties. We illustrate the benefits of our oblique rotation procedure by means of two illustrative examples.
对应分析（CA）是一种流行的方法，可用于分析类别变量之间的关系。它与几种流行的多元分析方法密切相关，如典型相关分析和主成分分析。与主成分分析一样，CA 解可以正交或斜交旋转到简单结构，而不会影响总解释惯性。然而，CA 的某些特定方面阻止了标准的旋转程序以简单的方式应用。特别是，点和维度分配的权重以及 CA 解的对偶性在 CA 中是独特的。对于正交简单结构旋转，最近已经提出了一些程序。在本文中，我们构建了考虑到这些特殊困难的 CA 斜交旋转方法。我们通过两个说明性示例来说明我们的斜交旋转过程的好处。
