二元分析
结构方程建模
单变量
路径分析(统计学)
潜变量
追踪
计算机科学
潜在增长模型
多元统计
路径(计算)
R包
数据挖掘
人工智能
机器学习
程序设计语言
作者
Zhiyong Zhang,Fumiaki Hamagami,Kevin J. Grimm,John J. McArdle
标识
DOI:10.1080/10705511.2014.935257
摘要
In this article, we introduce and demonstrate the application of a newly developed R package RAMpath for tracing path diagrams and conducting structural longitudinal data analysis. RAMpath was developed to preserve the essential features of the classic DOS version of the RAMpath program (McArdle & Boker, 1990) and ease data analysis done through structural equation modeling (SEM). The applicability of RAMpath is demonstrated through a mediation model, a MIMIC model, several latent growth curve models, a univariate latent change score model, and a bivariate latent change score model. In addition to performing regular SEM analysis, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based on results obtained from a bivariate dynamic system. RAMpath is provided as an open-source R package.
科研通智能强力驱动
Strongly Powered by AbleSci AI