A Stata Module for Grade of Membership (GoM) Analysis

描述(由申请人提供)： 这个项目将为STATA增加一个成员等级分析模块。隶属度(GOM)是一种降维工具，用于揭示类别数据中隐含的潜在结构，因此，当与包含许多类别变量的数据集一起使用时，尤其令人感兴趣，例如来自国家长期护理调查的数据。 目前还没有在任何一般用途的商业统计软件包中实施全球监测系统。这对希望适应GOM模型的应用研究人员和对GOM方法本身感兴趣的研究人员都是一个障碍。 第一阶段项目的目标是开发、测试和验证两个原型STATA程序，这两个程序适用于两种不同的GOM模型，即条件GOM模型和无条件GOM模型。原型不会在速度和效率上进行优化，而是将用于制定两个模型的参数估计的算法细节，并验证获得的结果的正确性。最终，这些原型将被转换为适合商业发布的软件，包括完整的文档。

DESCRIPTION (provided by applicant): This project will add to Stata a module for Grade of Membership analysis. Grade of Membership (GoM) is a dimension-reduction tool for uncovering latent structure implicit in categorical data and, as such, is of most interest when utilized with datasets containing many categorical variables, such as data from the National Long Term Care Survey. There is currently no implementation of GoM in any general purpose, commercial statistical software package. This has been a hindrance both to applied researchers wishing to fit GoM models and to researchers interested in GoM methodology itself. The goal of the Phase I project is to develop, test, and certify two prototype Stata programs which fit two varieties of the GoM model, the conditional GoM model and the unconditional GoM model. The prototypes will not be optimized for speed and efficiency, but will instead serve to work out the algorithmic details of parameter estimation for both models and to certify the correctness of obtained results. Ultimately, these prototypes will be converted into software suitable for commercial release, including full documentation.