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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-mixed-effects-models-with-r/
课程评论：没有评论
**课程名称：** 使用R进行线性混合效应模型 **课程概述：** 本课程为期7个会话，旨在教授学员使用R软件拟合、解释和评估线性混合效应模型的必要知识和技能。线性混合效应模型，也称为嵌套、分层、纵向、重复测量或时间与空间伪重复模型，是一种最小二乘模型拟合程序。其典型特征是包含两个（或更多）方差来源，从而导致预测自变量之间存在多种相关结构，这会影响它们对预测因变量的估计效应或关系。在估计线性混合效应模型的“拟合”和参数时，必须考虑这些多种方差来源和相关结构。 混合效应模型的结构可以是加性的、非线性的、指数的或二项式的，或者假设与预测变量存在各种其他“族”建模关系。然而，在本“实践”课程中，我们将重点关注线性混合效应模型，特别是如何： 1. 选择合适的线性模型； 2. 在R中表示该模型； 3. 估计模型； 4. （如有需要）比较、解释和报告结果； 5. 验证模型和模型假设。 此外，本课程还将解释如何拟合不同的相关结构到时间型和空间型伪重复模型，以正确调整误差项之间的非独立性。尽管课程会涉及相关的统计概念，但主要侧重于使用R实现混合效应模型，并提供大量的R脚本、真实数据集和现场演示。由于课程的第一部分是关于使用R执行统计命令和脚本的“实践”入门指南，因此无需R的先验经验即可成功完成本课程。
Linear Mixed-Effects Models with R is a 7-session course that teaches the requisite knowledge and skills necessary to fit, interpret and evaluate the estimated parameters of linear mixed-effects models using R software. Alternatively referred to as nested, hierarchical, longitudinal, repeated measures, or temporal and spatial pseudo-replications, linear mixed-effects models are a form of least-squares model-fitting procedures. They are typically characterized by two (or more) sources of variance, and thus have multiple correlational structures among the predictor independent variables, which affect their estimated effects, or relationships, with the predicted dependent variables. These multiple sources of variance and correlational structures must be taken into account in estimating the "fit" and parameters for linear mixed-effects models.The structure of mixed-effects models may be additive, or non-linear, or exponential or binomial, or assume various other ‘families' of modeling relationships with the predicted variables. However, in this "hands-on" course, coverage is restricted to linear mixed-effects models, and especially, how to: (1) choose an appropriate linear model; (2) represent that model in R; (3) estimate the model; (4) compare (if needed), interpret and report the results; and (5) validate the model and the model assumptions. Additionally, the course explains the fitting of different correlational structures to both temporal, and spatial, pseudo-replicated models to appropriately adjust for the lack of independence among the error terms. The course does address the relevant statistical concepts, but mainly focuses on implementing mixed-effects models in R with ample R scripts, ‘real' data sets, and live demonstrations. No prior experience with R is necessary to successfully complete the course as the first entire course section consists of a "hands-on" primer for executing statistical commands and scripts using R.
