Strategies for modeling a categorical variable allowing multiple category choices
Strategies for modeling a categorical variable allowing multiple category choices
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DOI：
10.1177/0049124101029004001
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发表时间：
2001-05-01
影响因子：
6.3
通讯作者：
Liu, I
中科院分区：
文献类型：
--
作者：
Agresti, A;Liu, I
本文讨论了当受试者可以选择类别的任何子集时，对类别变量进行建模的策略。对于c个结果类别，模型涉及c维二元响应，每个组件指示是否选择特定类别策略如下：（I）直接使用logit模型用于每个组件的边际分布;这说明了组件响应之间的依赖性，但不将依赖性视为模型的组成部分。(2)使用包含主题随机效应的logit模型来生成组件之间的依赖关系;这种方法受到限制，因为它意味着具有一定交换率的非负关联。(3)使用对数线性建模;准对称模型是有用的，但仅限于估计受试者内效应。边缘logit模型不太完全描述数据的依赖模式，但需要更少的假设，并更直接地关注最大实质利益的影响。
This article discusses strategies for modeling a categorical variable when subjects can select any subset of the categories. With c outcome categories, the models relate to a c-dimensional binary response, with each component indicating whether a particular category is chosen The strategies are the following: (I) Using logit models directly for the marginal distribution of each component; this accounts for dependence among the component responses but does not treat the dependence as an integral part of the model. (2) Using logit models containing subject random effects to generate the dependence among the components; this approach is limited by implying nonnegative associations having a certain exchangeability. (3) Using loglinear modeling; quasi-symmetric ones are useful but are limited to estimation of within-subject effects. Marginal logit models less fully describe the dependence patterns for the data but require fewer assumptions and focus more directly on the effects of greatest substantive interest.