The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights. Furthermore, visualization of the relative density of each of the data patterns associated with a class is possible. The authors illustrate this approach with real data examples of LC models with three and more classes.

中文翻译：

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使用距离分析 BIPLOTS 可视化潜在类模型

作者建议使用距离双标图的分类分析来可视化由潜在类 (LC) 模型产生的后验分类。使用这个多变量图，可以在两个（或三个）维度上可视化多个 LC 的轮廓，特别是类内和类间的变化，以及类的重叠或分离以及类权重。此外，与类相关联的每个数据模式的相对密度的可视化是可能的。作者通过具有三个或更多类的 LC 模型的真实数据示例说明了这种方法。