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Analysis of Categorical Data with R

Download Analysis of Categorical Data with R written by Christopher R. Bilder, Thomas M. Loughin in PDF format. This book is under the category Computers and bearing the isbn/isbn13 number 1439855676/9781439855676. You may reffer the table below for additional details of the book.

$19.99

Specifications

book-author

Christopher R. Bilder, Thomas M. Loughin

publisher

Chapman and Hall/CRC

file-type

PDF

pages

547 pages

language

English

asin

B00MMOIY5K

isbn10

1439855676

isbn13

9781439855676


Book Description

Learn How to Correctly Analyze Categorical Data

Analysis of Categorical Data with R (PDF) presents a modern account of categorical data analysis using the popular R software. It includes recent techniques of model building and assessment for binary; multicategory; and count response variables and discusses fundamentals; like odds ratio and probability estimation. The authors give detailed advice and standards on which procedures to use and why to use them.

The Use of R as Both a Data Analysis Method and a Learning Tool

Requiring no previous experience with R; the text provides an introduction to the vital features and functions of R. It includes numerous examples from medicine; psychology; ecology; sports and other areas; together with extensive R code and output. The authors use data simulation in R to assist readers understand the underlying assumptions of a procedure and then to analyse the procedure’s performance. They also provide many graphical demonstrations of the features and properties of various analysis methods.

Web Resource
The data sets and R programs from each example are accessible at www.chrisbilder.com/categorical. The programs include code used to make every plot and piece of output. Many of these programs include code to show additional features or to perform more detailed analyses than what is in the text. Designed to be used in tandem with the ebook; the website also exceptionally provides videos of the authors teaching a course on the subject. These videos incorporate live; in-class recordings; which instructors may find useful in a blended or flipped classroom setting. The videos are also suitable as a replacement for a short course.

REVIEWS

“… I really enjoyed reading it due to its exceptional examples and extensive R code…The ebook would be a great textbook for advanced undergraduate or postgraduate courses; particularly if training in R programming is also a learning objective. A self-learner with basic knowledge of categorical data analysis would find the ebook easy to follow…Given the series of in-class videos provided on the associated website and all the R code available online at http://chrisbilder.com/categorical/; there is no doubt that this ebook would be a great textbook. Personally I would like to congratulate Bilder and Loughin on the writing of this valuable ebook. Even before I had finished reading the ebook; I had already recommended it to my students. Now; I highly recommend this ebook to all readers.” ― Australian & New Zealand Journal of Statistics; April 2016

This ebook offers an extensive introduction to analysis of categorical data with R. The context in connection with a multitude of application areas such as ecology; biology; medicine and sports; just to name a few. Recent model-building techniques are covered…Within the book; R is used not only as a data analysis tool but also as a learning tool…The ebook takes an easy-to-understand approach by partnering practical explanations with many illustrative examples…To help college students apply their knowledge; the ebook has also provided an extensive number of exercises…The textbook can also be a very helpful reference.” ― International Statistical Review; April 2016

In summary; I think this ebook is well organized and clearly written. I really enjoyed reading it; though I did not have time to run all of the R codes by myself. I want to use this ebook as a textbook in a graduate course for CDA. This ebook has many advantages. Compared to other standard textbooks; its complete presentation of examples from many different research areas and the R codes would allow the college students (and other readers) to become experts in CDA in all fields. The use of the same examples throughout different chapters consistently gives an excellent process of data analysis. Furthermore; a broad set of exercises at the end of each chapter (more than 65 pages in all) that differ in scope and subject manner would be good supporting materials for improving practical experiences of real data analysis.” ― Biometrics; 71; December 2015

… a valuable asset to any person who wants to evaluate categorical data. Bilder and Loughin decode categorical data analysis using a simple approach; with sufficient statistical theory to allow the reader to understand the underlying assumptions of the analyses involved; but with minimal; unintimidating mathematical symbols; and equations. The authors have managed to explain the statistics involved in categorical data analysis in unadorned semantics and supplemented them with corresponding R codes …. This is a major plus for this ebook. Overall; the ebook is well written: It contains easy-to-follow R codes; footnote explanations of material that could not be explained within the text; and numerous exercises at the end of each chapter. … Excellent videos of Bilder teaching the material in class; full R codes; and corresponding data; each arranged by chapter; are accessible on a website. These resources make it easy for readers to get a deeper understanding of categorical data analysis. … This ebook is a must-have tool for any biostatistician analyzing categorical data in R. It could very well be used as a text in intermediate-to-advanced applied courses in practical analysis of categorical data.” ― Biometrical Journal; 57; 2015

Bilder and Loughin have worked as a dynamic duo for a number of years; and they clearly are combining their knowledge; talents; experience; and teamwork to create this valuable ebook. Analyzing categorical data correctly and in-depth is not as easy as it appears in many courses and textbooks. As an outcome; many people can get the wrong idea about what could and should be done with categorical data; and thus their results can be inconclusive or incorrect. This ebook gives users the full scoop when it comes to analyzing categorical data of all types; and it does so in an easy-to-understand way; giving confidence to the reader to go ahead and implement the ideas in practice. The use of R for analyzing data is becoming a worldwide phenomenon and a staple for data analysts on every level. As its popularity grows; it becomes important for beginners to become comfortable with understanding and using R to analyze their data. Through the special attention paid to teaching the basics of R; along with providing step-by-step particulars in using R in each separate analysis; Bilder and Loughin help establish and promote a group of confident; comfortable users of this software that can seem a problem to many. I highly and happily recommend this ebook to anyone who intends to analyze categorical data in their careers―which includes most all of us!” ― Deborah J. Rumsey; Ph.D.; Auxiliary Professor and Statistics Education Specialist; Department of Statistics; The Ohio State University

NOTE: The product includes the ebook; Analysis of Categorical Data with R. in PDF. No access codes are included.

book-author

Christopher R. Bilder, Thomas M. Loughin

publisher

Chapman and Hall/CRC

file-type

PDF

pages

547 pages

language

English

asin

B00MMOIY5K

isbn10

1439855676

isbn13

9781439855676

Table of contents


Table of contents :

Content: Analyzing a Binary Response, Part 1: Introduction One binary variable Two binary variables Analyzing a Binary Response, Part 2: Regression Models Linear regression models Logistic regression models Generalized linear models Analyzing a Multicategory Response Multinomial probability distribution I x J contingency tables and inference procedures Nominal response regression models Ordinal response regression models Additional regression models Analyzing a Count Response Poisson model for count data Poisson regression models for count responses Poisson rate regression Zero inflation Model Selection and Evaluation Variable selection Tools to assess model fit Overdispersion Examples Additional Topics Binary responses and testing error Exact inference Categorical data analysis in complex survey designs “Choose all that apply” data Mixed models and estimating equations for correlated data Bayesian methods for categorical data Appendix A: An Introduction to R Appendix B: Likelihood Methods Bibliography Index Exercises appear at the end of each chapter.
Abstract: “We live in a categorical world! From a positive or negative disease diagnosis to choosing all items that apply in a survey, outcomes are frequently organized into categories so that people can more easily make sense of them. However, analyzing data from categorical responses requires specialized techniques beyond those learned in a first or second course in Statistics. We o er this book to help students and researchers learn how to properly analyze categorical data. Unlike other texts on similar topics, our book is a modern account using the vastly popular R software. We use R not only as a data analysis method but also as a learning tool. For example, we use data simulation to help readers understand the underlying assumptions of a procedure and then to evaluate that procedure’s performance. We also provide numerous graphical demonstrations of the features and properties of various analysis methods. The focus of this book is on the analysis of data, rather than on the mathematical development of methods. We o er numerous examples from a wide rage of disciplines medicine, psychology, sports, ecology, and others and provide extensive R code and output as we work through the examples. We give detailed advice and guidelines regarding which procedures to use and why to use them. While we treat likelihood methods as a tool, they are not used blindly. For example, we write out likelihood functions and explain how they are maximized. We describe where Wald, likelihood ratio, and score procedures come from. However, except in Appendix B, where we give a general introduction to likelihood methods, we do not frequently emphasize calculus or carry out mathematical analysis in the text. The use of calculus is mostly from a conceptual focus, rather than a mathematical one”

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