Circular statistics in R / Arthur Pewsey, Markus Neuhäuser, Graeme D. Ruxton.

Format:

Book

Author/Creator:

Pewsey, Arthur.

Contributor:

Neuhäuser, Markus.

Ruxton, Graeme D.

Language:

English

Subjects (All):

Circular data--Data processing.

Circular data.

Statistics--Data processing.

Statistics.

R (Computer program language).

Physical Description:

1 online resource (192 pages)

Edition:

First edition.

Place of Publication:

Oxford, United Kingdom : Oxford University Press, 2013.

Language Note:

English

Summary:

Measurements like mass, length and speed are 'linear'; but compass direction or the time of the year are 'circular'. Circular data have a repeating nature and an arbitrary zero: 12 months after the 1st of July it is the 1st of July again. This book explains how to easily and effectively analyse circular data statistically. 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 whether it be angular directions such as: observed compass directions of departure of radio-collared migratory birds from a release point; bond angles measured in different molecules; wind directions at different times of year at a wind farm; direction of stress-fractures in concrete bridge supports;longitudes of earthquake epicentres or seasonal and daily activity patterns, for example: data on the times of day at which animals are caught in a camera trap, or in 911 calls in New York, or in internet traffic; variation throughout the year in measles incidence, global energy requirements, TV viewing figures orinjuries to athletes. 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. They go on to discuss basic forms of inference such as point and interval estimation, as well as formal significance tests for hypotheses that will often be of scientific interest.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, particularly its " package. Also provided are over 150 new functions for techniques not already covered within R. This concise but authoritative guide is accessible to the diverse range of scientists who have circular data to analyse and want to do so as easily and as effectively as possible.

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.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

0-19-165077-3

0-19-165076-5

OCLC:

1336402564