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所在平台: Udemy |
课程主页: https://www.udemy.com/course/comprehensive-linear-modeling-with-r/
课程评论：没有评论
课程名称：全面线性建模与R 课程概述：全面线性建模与R课程提供了多种当代线性和非线性建模方法的广泛概述，旨在分析研究数据。课程内容涵盖基本推断、条件推断及同时推断技术；方差分析（ANOVA）；线性回归；生存分析；广义线性模型（GLM）；参数和非参数平滑器及广义加性模型（GAM）；纵向数据和混合效应模型，分割实验设计以及其他嵌套模型设计。课程展示了如何使用R Commander来执行这些任务，R Commander是一个基于图形用户界面的R软件前端，支持多种统计和图形技术的实现。 课程从快速概述不同的图形绘制技术开始，接着回顾基本推断和条件推断的方法，然后进行方差分析。接下来，课程介绍线性回归及验证线性模型的章节，详细讲解广义线性建模（GLM）并通过实际研究数据进行演示。此外，课程还包括生存分析的线性和非线性模型、平滑器及广义加性模型（GAM）、使用广义估计方程（GEE）的纵向模型、混合效应模型、分割实验设计和嵌套设计的部分。课程通过各种图形展示验证线性模型并比较替代模型，从而选择“最佳”模型。 该课程内容丰富，涵盖线性（及部分非线性）建模方法，适合初学者、中级用户和高级R用户，特别是研究生及数据分析专业人士，他们在职业中需要进行线性和非线性建模。
Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. The course showcases the use of R Commander in performing these tasks. R Commander is a popular GUI-based "front-end" to the broad range of embedded statistical functionality in R software. R Commander is an 'SPSS-like' GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK+) which is known to have problems running on a Mac computer.The course progresses through dozens of statistical techniques by first explaining the concepts and then demonstrating the use of each with concrete examples based on actual studies and research data. Beginning with a quick overview of different graphical plotting techniques, the course then reviews basic approaches to establish inference and conditional inference, followed by a review of analysis of variance (ANOVA). The course then progresses through linear regression and a section on validating linear models. Then generalized linear modeling (GLM) is explained and demonstrated with numerous examples. Also included are sections explaining and demonstrating linear and non-linear models for survival analysis, smoothers and generalized additive models (GAMs), longitudinal models with and without generalized estimating equations (GEE), mixed-effects, split-plot, and nested designs. Also included are detailed examples and explanations of validating linear models using various graphical displays, as well as comparing alternative models to choose the 'best' model. The course concludes with a section on the special considerations and techniques for establishing simultaneous inference in the linear modeling domain.The rather long course aims for complete coverage of linear (and some non-linear) modeling approaches using R and is suitable for beginning, intermediate and advanced R users who seek to refine these skills. These candidates would include graduate students and/or quantitative and/or data-analytic professionals who perform linear (and non-linear) modeling as part of their professional duties.
