Department of Human Ecology, University of California, Davis.
Department of Human Development and Family Studies, The Pennsylvania State University.
Multivariate Behav Res. 2021 Nov-Dec;56(6):941-955. doi: 10.1080/00273171.2020.1794775. Epub 2020 Aug 28.
Mixture modeling is commonly used to model sample heterogeneity by identifying unobserved classes of individuals with similar characteristics. Despite abundance of evidence in the literature suggesting that individuals are often characterized by different dynamic processes underlying their physiological, cognitive, psychological, and behavioral states, applications of dynamic mixture modeling are surprisingly lacking. We present here a proof-of-concept example of dynamic mixture modeling, where latent groups of individuals were identified based on different dynamic patterns in their time series. Our sample consists of 192 men who were in a heterosexual relationship. They were asked to complete a daily questionnaire involving emotions related to their relationship. Two latent groups were identified based on the strength of association between positive (e.g., loving) and negative (e.g., doubtful) affect. Men in the group characterized by a strong negative association () tended to be younger and had higher levels of anxiety toward their relationship than men in the other group, which was characterized by a weaker negative association (). We illustrate the specification and estimation of dynamic mixture model using "dynr," an R package capable of handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties.
混合模型常用于通过识别具有相似特征的个体的未观察到的类别来模拟样本异质性。尽管文献中有大量证据表明，个体通常以不同的动态过程为特征，这些过程是其生理、认知、心理和行为状态的基础，但动态混合模型的应用却出奇地缺乏。我们在这里提出了一个动态混合模型的概念验证示例，其中根据个体时间序列中的不同动态模式来识别潜在的群体。我们的样本包括 192 名男性，他们处于异性关系中。他们被要求完成一份涉及与他们的关系相关的情绪的每日问卷。根据积极（如“爱”）和消极（如“怀疑”）情绪之间的关联强度，确定了两个潜在的群体。在特征为强烈负相关（）的组中，男性往往更年轻，对他们的关系的焦虑水平高于另一个组，后者的特征是负相关较弱（）。我们使用“dynr”来说明动态混合模型的规范和估计，这是一个 R 包，能够处理具有转换特性的广泛的线性和非线性离散和连续时间模型。
