Journal Article10.1007/S00180-011-0261-5
Boxplot for circular variables
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TL;DR: A boxplot version for a circular data set, called the circular boxplot is proposed, which performs better when both the value of κ and the sample size are larger and is developed in S-Plus.
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Abstract: A boxplot is a simple and flexible graphical tool which has been widely used in exploratory data analysis. One of its main applications is to identify extreme values and outliers in a univariate data set. While the boxplot is useful for a real line data set, it is not suitable for a circular data set due to the fact that there is no natural ordering of circular observations. In this paper, we propose a boxplot version for a circular data set, called the circular boxplot. The problem of finding the appropriate circular boxplot criterion of the form ν × CIQR, where CIQR is the circular interquartile range and ν is the resistant constant, is investigated through a simulation study. As might be expected, we find that the choice of ν depends on the value of the concentration parameter κ. Another simulation study is done to investigate the performance of the circular boxplot in detecting a single outlier. Our results show that the circular boxplot performs better when both the value of κ and the sample size are larger. We develop a visual display for the circular boxplot in S-Plus and illustrate its application using two real circular data sets.
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A boxplot for circular data.
TL;DR: A Tukey-like circular boxplot is introduced, which would be especially useful in all fields where circular measures arise: biometrics, astronomy, environmetrics, Earth sciences, to cite just a few.
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Dendritic-branching angles of pyramidal neurons of the human cerebral cortex.
Pablo Fernandez-Gonzalez,Ruth Benavides-Piccione,Ignacio Leguey,Concha Bielza,Pedro Larrañaga,Javier DeFelipe +5 more
TL;DR: It is found that common design principles exist and govern the patterns found in the different branches that compose the basal dendrites of human pyramidal cells of the temporal cortex, but particular differences were found between supra and infragranular cells.
References
7. Statistical Analysis of Circular Data
TL;DR: Preface 1. Introduction 2. The purpose of the book 2. Descriptive Methods: 2.1. Data display 2.2. Simple summary quantities 2.3. Notation trigonometric moments 3. Models: 3. Survey of contents 3.4. Exploratory analysis 4. Analysis of a Single Sample of Data: 4.1 Statistical analysis of a random sample of unit vectors from a multimodal distribution .
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