Textual data science with R, MónicaBécue‐Bertaut, Boca Raton: CRC Press.
Textual data science with R, MónicaBécue‐Bertaut, Boca Raton: CRC Press.
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R 文本数据科学，MónicaBécueâBertaut，博卡拉顿：CRC Press。
DOI：
10.1111/biom.13181
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发表时间：
2019
期刊：
影响因子：
1.9
通讯作者：
Sánchez, Brisa N.
中科院分区：
文献类型：
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作者：
Sánchez, Brisa N.
读者有兴趣找到一个广泛的介绍与时空结构的统计方法将找到大量的资源在名义上的工作。内容分为六章，第一章提供了本书的动机，其余部分遵循典型分析的工作流程。因此，第2章详细介绍了该领域的常见数据结构、可视化和探索工具。然后，作者从一般的（第3章）时空模型，他们定义为描述性的（第4章）和动态（第5章）时空模型。他们将这种区分作为区分如何查看和使用模型的一种方式。后一点更具体地集中在预测和推理之间的区别上，这一点在“大数据”的兴起使两者之间的界限变得越来越模糊的时候得到了很好的体现。对于任何分析工作流程，本书最后概述了用于评估和比较前几章中开发的模型的技术和工具。
Readers interested in finding a broad introduction to statistical methods with spatiotemporal structure will find ample resources in the titular work. The contents are laid out in six chapters, with the first providing motivation for the book and the rest following the workflow of a typical analysis. Thus, Chapter 2 details common data structures, visualization, and exploratory tools typical to the area. The authors then build up from general (Chapter 3) spatiotemporal models to what they define as descriptive (Chapter 4) and dynamic (Chapter 5) spatiotemporal models. They make this distinction as a way to differentiate both how models can be viewed and used. The latter point focusing more specifically on the difference between prediction and inference—a point that’s well made at a time when the rise of “Big Data” has made the line separating the two increasingly blurry. True to any analysis workflow, the book finishes with an overview of techniques and tools for evaluating and comparing the models developed in the previous chapters.