![]() ![]() ![]() Two illustrative applications, one concerning the locomotion of a Drosophila fly larva, the other analyzing a large set of sudden infant death syndrome data, are investigated. Circular Statistics in R provides the most comprehensive guide to the analysis of circular data in over a decade. Profile likelihoods come to the fore in practice. Further, the location parameter can sometimes be made approximately orthogonal to all the other parameters. Explore the science of life by learning about the systems and structures that make up the organisms of our world. ![]() As well as a variety of interesting theoretical properties, when likelihood inference is explored these distributions display orthogonality between elements of a pairing of parameters into (location, skewness) and (concentration, peakedness). The skewness transformation is especially appealing as it has no effect on the normalizing constant. The key is to employ inverses of Batschelet-type transformations in certain ways these exhibit considerable advantages over direct Batschelet transformations. Our approach is to transform the scale of a generating distribution, such as the von Mises, using various nontrivial extensions of an approach first used in Batschelet’s (1981, Circular Statistics in Biology) book. We provide four-parameter families of distributions on the circle which are unimodal and display the widest ranges of both skewness and peakedness yet available. Circular Statistics in R provides the most comprehensive guide to the analysis of circular data in over a decade. Quick and easy for readers with all levels of experience with circular data to find relevant and reliable advice on the most commonly-used analyses. Inverse Batschelet distributions for circular data. Arthur Pewsey, Markus Neuhauser, and Graeme D Ruxton. ![]()
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