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OpenMx 2.0:扩展结构方程与统计建模

OpenMx 2.0: Extended Structural Equation and Statistical Modeling.

作者信息

Neale Michael C, Hunter Michael D, Pritikin Joshua N, Zahery Mahsa, Brick Timothy R, Kirkpatrick Robert M, Estabrook Ryne, Bates Timothy C, Maes Hermine H, Boker Steven M

机构信息

Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, USA.

Department of Psychology, University of Oklahoma, Norman, USA.

出版信息

Psychometrika. 2016 Jun;81(2):535-49. doi: 10.1007/s11336-014-9435-8. Epub 2015 Jan 27.

Abstract

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

摘要

介绍了用于结构方程和其他统计建模的新软件包OpenMx 2.0,并描述了其功能。OpenMx正朝着模块化方向发展,现在允许一种混合搭配的计算方法,该方法将模型期望与拟合函数和优化器分开。主要的后端架构改进包括转向可互换的开源优化器,如新编写的CSOLNP。已经实现了诸如项目因子分析和状态空间建模等全新的方法。新的模型期望函数包括对LISREL语法中模型表达式的支持以及简化的多组期望函数。易用性方面的改进包括用于标准化模型参数并计算其基于雅可比矩阵的标准误差的辅助函数、通过标准R $机制访问模型组件,以及在R图形用户界面中改进的自动补全功能。

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