HMMTree: A computer program for latent-class hierarchical multinomial processing tree models
HMMTree: A computer program for latent-class hierarchical multinomial processing tree models
复制标题
HMMTree：用于潜在类分层多项式处理树模型的计算机程序
DOI：
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发表时间：
2007
影响因子：
5.4
通讯作者：
K. C. Klauer
中科院分区：
文献类型：
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作者：
Christoph Stahl;K. C. Klauer
潜在类层次多项式模型是广泛使用的多项处理树模型家族的重要扩展，因为它们允许测试参数同质性假设并为参数异质性建模提供框架。本文介绍了计算机程序HMMTree，作为实现潜在类分层多项式处理树模型的一种手段。HMMTree计算这些模型的参数估计、置信区间和拟合优度统计，以及Fisher信息、期望类别均值和方差，以及类隶属度的后验概率。提供了使用该程序的简要指南。
Latent-class hierarchical multinomial models are an important extension of the widely used family of multinomial processing tree models, in that they allow for testing the parameter homogeneity assumption and provide a framework for modeling parameter heterogeneity. In this article, the computer program HMMTree is introduced as a means of implementing latent-class hierarchical multinomial processing tree models. HMMTree computes parameter estimates, confidence intervals, and goodness-of-fit statistics for such models, as well as the Fisher information, expected category means and variances, and posterior probabilities for class membership. A brief guide to using the program is provided.