03646cam a22004697i 4500999001900000001000900019003000800028005001700036008004100053010001700094020001800111020001500129035002400144040008400168042001400252050002100266082001400287100003700301245015400338250001900492264005800511300004500569336002600614337002800640338002700668504006700695505031400762520058801076520058601664520028902250650002702539650003502566650004202601700004102643700004002684856009102724856009202815856010602907906004503013942000803058952011003066 c430350d43035017741205AT-ISTA20201022154758.0130516s2013 enka b 001 0 eng a 2013940576 a9780199671137 a0199671133 a(OCoLC)ocn865566482 aDLCbengcCUDerdadOCLCOdIADdIXAdORUdIPLdIADdUWWdBTCTAdYDXCPdBDXdDLC alccopycat00aQA276b.P45 201304a519.52231 aPewsey, Arthur,eauthor.921524010aCircular statistics in R /cArthur Pewsey, University of Extremadura, Markus Neuhaeuser, RheinAhrCampus, Graeme D. Ruxton, University of St. Andrews. aFirst edition. 1aOxford ;aNew York :bOxford University Press,c2013. axiv, 183 pages :billustrations ;c24 cm atextbtxt2rdacontent aunmediatedbn2rdamedia avolumebnc2rdacarrier aIncludes bibliographical references (pages 173-178) and index.0 aIntroduction -- 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. a"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.8 a"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.8 a"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. 0aCircular data.9215241 0aMathematical statistics.91277 0aR (Computer program language)92152421 aNeuhaeuser, Markus,eauthor.92152431 aRuxton, Graeme D.,eauthor.9202107423Publisher descriptionuhttp://www.loc.gov/catdir/enhancements/fy1410/2013940576-d.html413Table of contents onlyuhttp://www.loc.gov/catdir/enhancements/fy1410/2013940576-t.html423Contributor biographical informationuhttp://www.loc.gov/catdir/enhancements/fy1410/2013940576-b.html a7bcbcccopycatd2encipf20gy-gencatlg 2ddc 00102ddc4070aLIBbLIBd2020-10-22e3g32.22o519pAT-ISTA#002168r2020-10-22v32.22w2020-10-22yBOOK