Circular statistics in R / Arthur Pewsey, University of Extremadura, Markus Neuh�auser, RheinAhrCampus, Graeme D. Ruxton, University of St. Andrews.

By: Pewsey, Arthur [author.]
Contributor(s): Neuh�auser, Markus [author.] | Ruxton, Graeme D [author.]
Material type: TextTextPublisher: Oxford ; New York : Oxford University Press, 2013Edition: First editionDescription: xiv, 183 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780199671137; 0199671133Subject(s): Circular data | Mathematical statistics | R (Computer program language)DDC classification: 519.5 LOC classification: QA276 | .P45 2013Online resources: Publisher description | Table of contents only | Contributor biographical information
Contents:
Introduction -- Graphical representation of circular data -- Circular summary statistics -- Distribution theory and models for circular random variables -- Basic inference for a single sample -- Model fitting for a single sample -- Comparing two or more samples of circular data -- Correlation and regression.
Summary: "Circular Statistics in R provides the most comprehensive guide to the analysis of circular data in over a decade. Circular data arise in many scientific contexts, both from angular observations, and from daily or seasonal activity patterns. ... The natural way of representing such data graphically is as points located around the circumference of a circle, hence their name. Importantly, circular variables are periodic in nature, and the origin, or zero point, such as the beginning of a new year, is defined arbitrarily rather than necessarily emerging naturally from the system.Summary: "This book will be of value both to those new to circular data analysis as well as those more familiar with the field. For beginners, the authors start by considering the fundamental graphical and numerical summaries used to represent circular data before introducing distributions that might be used to model them. When discussing model fitting, the authors advocate reduced reliance on the classical von Mises distribution, showcasing distributions that are capable of modelling features such as asymmetry and varying levels of kurtosis that are often exhibited by circular data.Summary: "The use of likelihood-based and computer-intensive approaches to inference and modelling are stressed throughout the book. The R programming language is used to implement the methodology. Also provided are over 150 new functions for techniques not already covered in R."--Back cover.
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Includes bibliographical references (pages 173-178) and index.

Introduction -- Graphical representation of circular data -- Circular summary statistics -- Distribution theory and models for circular random variables -- Basic inference for a single sample -- Model fitting for a single sample -- Comparing two or more samples of circular data -- Correlation and regression.

"Circular Statistics in R provides the most comprehensive guide to the analysis of circular data in over a decade. Circular data arise in many scientific contexts, both from angular observations, and from daily or seasonal activity patterns. ... The natural way of representing such data graphically is as points located around the circumference of a circle, hence their name. Importantly, circular variables are periodic in nature, and the origin, or zero point, such as the beginning of a new year, is defined arbitrarily rather than necessarily emerging naturally from the system.

"This book will be of value both to those new to circular data analysis as well as those more familiar with the field. For beginners, the authors start by considering the fundamental graphical and numerical summaries used to represent circular data before introducing distributions that might be used to model them. When discussing model fitting, the authors advocate reduced reliance on the classical von Mises distribution, showcasing distributions that are capable of modelling features such as asymmetry and varying levels of kurtosis that are often exhibited by circular data.

"The use of likelihood-based and computer-intensive approaches to inference and modelling are stressed throughout the book. The R programming language is used to implement the methodology. Also provided are over 150 new functions for techniques not already covered in R."--Back cover.

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