[R-sig-ME] Book: Mixed and Phylogenetic Models: A Conceptual Introduction to Correlated Data
Anthony R. Ives
@rive@ @ending from wi@c@edu
Tue Oct 2 17:07:28 CEST 2018
In case it might be useful, I recently self-published a tutorial on mixed and phylogenetic models:
Mixed and Phylogenetic Models: A Conceptual Introduction to Correlated Data
Anthony R. Ives
You can download it for free at https://leanpub.com/correlateddata. And please, do get it for free. I'm just using leanpub.com because it provides a nice platform and notifies downloaders when I update the book.
This book introduces the concepts behind statistical methods used to analyze data with correlated error structures. While correlated data arise in many ways, the focus is on ecological and evolutionary data, and two types of correlations: correlations generated by the hierarchical nature of the sampling (e.g., plots sampled within sites) and correlations generated by the phylogenetic relationships among species.
The book is integrated with R code that illustrates every point. Although it is possible to read the book without the code, or work through the code without the book, they are designed to go hand-in-hand. The R code comes with the complete downloadable package of the book on leanpub.com; if you have problems downloading it, please contact me.
Chapter 1, Multiple Methods for Analyzing Hierarchical Data
Chapter 2, Good Statistical Properties
Chapter 3, Phylogenetic Comparative Methods
Chapter 4, Phylogenetic Community Ecology
Background you'll need
Although the book is titled an introduction, it is an introduction to the concepts behind the methods discussed, not so much the methods themselves. It assumes that you understand basic statistical concepts (such as random variables) and know R and how to run mixed and phylogenetic models. I think that in many cases, the best way of learning is by doing. On the other hand, there is no substitute for getting a good background in the basics of statistical analyses and R before launching off into the more complicated material in this book.
This book is the product of many people. The general ideas come from a class I teach at UW-Madison for graduate students, and they have all had a huge impact on how I think about and try to explain statistics. The more proximate origin of the book is a workshop I gave in 2018 at the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, which followed the same outline. Participants in this workshop provided great help in honing the content and messages. I am indebted to Professors Chen Jin and Wang Bo for hosting my visit.
I also thank Li Daijiang for all of his work developing, cleaning, and speeding the `communityPGLMM()` code that is the main tool used for Chapter 4. I wish I had his skills. Michael Hardy also kindly allowed me to model the example used in Chapters 1 and 2 on his real dataset. Li Daijiang, Joe Phillips, Tanjona Ramiadantsoa, and Xu Fangfang provided thoughtful comments on parts or all of the manuscript, although I'm responsible for all the lingering errors.
Finally, this work has been supported by the National Science Foundation through various grants, and I am very grateful for this support.
Anthony R. Ives
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