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所在平台: Udemy |
课程主页: https://www.udemy.com/course/plspathmodelingwiththesemplsandplspmpackagesinr/
课程评论：没有评论
**课程名称：** 使用 R 中的 semPLS 和 PLSPM 包进行 PLS 路径建模 **课程概述：** 本课程旨在全面介绍 R 语言中 semPLS 和 PLSPM 这两个软件包的核心功能和应用。虽然这两个 R 包与 SmartPLS 共享相同的 PLS 算法，能够生成几乎一致的 PLS 模型估计结果（除少数特殊情况），但它们各自还包含许多其他有价值且互补的附加功能。 **semPLS 包的亮点：** * 提供引人注目的 PLS 路径模型估计图表。 * 能够将模型转换为可在基于协方差的其他 R 函数中运行，这极具实用性。 **PLSPM 包的亮点：** * 提供全面且格式良好的 PLS 输出，符合学术发表所需的表格和报告标准。 * 具备非常实用且独特的多组调节分析能力。 * 独有的 REBUS-PLS 功能，可用于发现数据中的异质性（更深入的多组差异分析）。 **学习建议：** 如果您对 PLS 路径建模有浓厚兴趣，深入了解 semPLS 和 PLSPM 这两个 R 包将是您宝贵的学习经历。掌握这两个工具将极大地提升您在 PLS 路径建模领域的分析和应用能力。
The course PLS Path Modeling with the semPLS and PLSPM packages in R demonstrates the major capabilities and functions of the R semPLS package; and the major capabilities and functions of the R PLSPM package. Although the semPLS and plspm R packages use the same PLS algorithm as does SmartPLS, and consequently produce identical PLS model estimates (in almost all cases with a few exceptions), each of the two R packages also contains additional, useful, complementary functions and capabilities. Specifically, semPLS has some interesting plots and graphs of PLS path model estimates and also converts your model to run in covariance-based R functions (which is quite handy!). On the other hand, the PLSPM package has very complete and well-formatted PLS output that is consistent with the tables and reports required for publication, and also has very useful and unique multigroup-moderation analysis capabilities, and a unique REBUS-PLS function for discovering heterogeneity (more multi-group differences). If you are interested in knowing a lot about PLS path modeling, it is certainly a good use of your time to become familiar with both the semPLS and PLSPM packages in R.
