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所在平台: Udemy |
课程主页: https://www.udemy.com/course/survival-analysis-in-r/
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课程名称：R语言生存分析 课程概述：生存分析是统计学的一个子领域，常被称为事件时间分析、可靠性分析或持续时间分析。R语言因其存活包而成为执行此类分析的主要工具。在本课程中，您将学习如何使用R进行生存分析。建议您查看课程大纲以获取详细内容，并可通过免费预览视频了解更多信息。 课程结构：课程开始时将进行课程导向，介绍用于生存分析的主要包以及如何找到它们，课程数据集和一般生存分析概念。随后，我们将开始创建第一个生存模型，使用卡普兰-梅耶估计量和对数秩检验等标准工具进行分析。生存分析中一个至关重要的模型类型是Cox比例风险模型，您将学习如何构建此模型、添加协变量及其结果的解释。 此外，您将学习生存树，这是一种逐渐流行的机器学习工具，R语言提供了多个函数用于拟合生存树。课程的最后两个部分将帮助您为分析准备数据集。许多情况下，日期时间数据需要正确格式化才能使用，因此专门增加了关于日期时间处理的部分，重点介绍lubridate包。同时，您还将学习如何检测和替换缺失值及异常值，这些问题数据可能会严重影响分析，因此掌握管理方法至关重要。 除了视频、代码和数据集，您还可以访问专门讨论生存分析的活跃讨论区。值得一提的是，此课程属于数据科学课程组合的一部分，您可以查看R教程的讲师页面，了解更多课程。全球已有超过10万人利用我们的课程掌握数据科学，您也可以试试看！结合Udemy的30天退款保证，您无需担心风险，只需获得宝贵的技能，提升在当今就业市场的竞争力。
Survival Analysis is a sub discipline of statistics. It actually has several names. In some fields it is called event-time analysis, reliability analysis or duration analysis. R is one of the main tools to perform this sort of analysis thanks to the survival package.In this course you will learn how to use R to perform survival analysis. To check out the course content it is recommended to take a look at the course curriculum. There are also videos available for free preview.The course structure is as follows:We will start out with course orientation, background on which packages are primarily used for survival analysis and how to find them, the course datasets as well as general survival analysis concepts.After that we will dive right in and create our first survival models. We will use the Kaplan Meier estimator as well as the logrank test as our first standard survival analysis tools.When we talk about survival analysis there is one model type which is an absolute cornerstone of survival analysis: the Cox proportional hazards model. You will learn how to create such a model, how to add covariates and how to interpret the results.You will also learn about survival trees. These rather new machine learning tools are more and more popular in survival analysis. In R you have several functions available to fit such a survival tree.The last 2 sections of the course are designed to get your dataset ready for analysis. In many scenarios you will find that date-time data needs to be properly formatted to even work with it. Therefore, I added a dedicated section on date-time handling with a focus on the lubridate package. And you will also learn how to detect and replace missing values as well as outliers. These problematic pieces of data can totally destroy your analysis, therefore it is crucial to understand how to manage it.Besides the videos, the code and the datasets, you also get access to a vivid discussion board dedicated to survival analysis.By the way, this course is part of a whole data science course portfolio. Check out the R-Tutorials instructor page to see all the other available course.Well over 100.000 people around the world did already use our classes to master data science. Why don´t you try it out yourself? With a Udemy 30-day money back guarantee there is nothing you can lose, you can only gain precious skills to come out ahead in today's job market.
