当前位置: X-MOL 学术Struct. Equ. Model. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots
Structural Equation Modeling: A Multidisciplinary Journal ( IF 2.5 ) Pub Date : 2015-06-26 , DOI: 10.1080/10705511.2014.979932
Michael C Neale 1 , Shaunna L Clark 1 , Conor V Dolan 2 , Michael D Hunter 3
Affiliation  

A linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of first-order Markov switching probabilities was specified to be time-invariant, regardless of the pair of occasions being considered. The first extension, time-varying transitions, specifies different Markov transition matrices between each pair of occasions. The second extension is second-order time-invariant Markov transition probabilities, such that the probability of switching depends on the states at the 2 previous occasions. The models are implemented using the R package OpenMx, which facilitates data handling, parallel computation, and further model development. It also enables the extraction and display of relative likelihoods for every individual in the sample. The models are illustrated with previously published data on alcohol use observed on 4 occasions as part of the National Longitudinal Survey of Youth, and demonstrate improved fit to the data.

中文翻译:


药物使用的制度切换建模:时变和二阶马尔可夫模型和个体概率图



具有状态切换的线性潜在生长曲线混合模型以两种方式扩展。以前,一阶马尔可夫切换概率矩阵被指定为时不变的,无论考虑的场合对如何。第一个扩展是时变转换,指定每对场合之间不同的马尔可夫转换矩阵。第二个扩展是二阶时不变马尔可夫转移概率,使得切换的概率取决于前两次的状态。这些模型是使用 R 包 OpenMx 实现的,该包有利于数据处理、并行计算和进一步的模型开发。它还可以提取和显示样本中每个人的相对可能性。这些模型以之前发布的全国青年纵向调查中 4 次观察到的饮酒数据进行说明,并证明了与数据的拟合度有所提高。
更新日期:2015-06-26
down
wechat
bug