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Circular Statistics in R (Paperback)
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Circular Statistics in R (Paperback)
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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 or
injuries 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
"circular" 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.
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