R数据分析：再写潜在类别分析LCA的做法与解释

应粉丝要求，再给大家写一期潜在类别分析的教程，尽量写的详细一点。

首先，问题导入，啥是潜在类别分析？

Latent Class Analysis (LCA) is a statistical model in which individuals can be classified into mutually exclusive and exhaustive types, or latent classes, based on their pattern of answers on a set of (categorical) measured variables.

潜在类别分析就是依据个体在分类变量上的响应，将个体分为互斥的组，群，潜类别。

在这儿，组，群，潜类别都是一个东西，这儿大家注意，在潜在类别分析中响应变量或者说显变一定是分类变量，这个要和潜在剖面分析LPA区别开。

在做潜类别的时候你首先要设定你要你的数据有几个潜类别，我们的标准是拟合好的情况下尽可能选择最少的潜类别。

这儿值得注意的是，在R语言种poLCA的作者说过这么一段话：

He said, that he wouldn´t rely on statistical criteria to decide which model is the best, but he would look which model has the most meaningful interpretation and has a better answer to the research question.