ClassViolinPlot

Data

Optional: The sequence of colors used, Default: DefaultColorSequence()

ColorSequence

Optional: The names of the classes. Default: C1 - C(Number of Classes)

ClassNames

Optional: Add a legent to plot. Default: TRUE)

PlotLegend

Optional: Title of the plot. Default: "ClassViolinPlot""

main

Optional: Title of the x axis. Default: "Classes"

xlab

Optional: Title of the y axis. Default: "Data"

ylab

ClassViolinPlot the data for all classes

A collection of various visualizations methods is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through the combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). The visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables, the Shepard density plot as well as the Bland-Altman plot are presented here. With regards to classified high-dimensional data several visualizations are presented, e.g. the heat map and silhouette plot. For a classification of countries, a map of the world or Germany can be visualized. More detailed explanations can be found in the book of Thrun, M.C.:"Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>. Furthermore, for categorical features the ABC analysis improved Pie charts, slope charts and fan plots are usable. Additionally, for measurements across a geographic area an easy to use function for a Choropleth map is presented here. The flagship of this package is the mirrored density plot (MD-plot) which is a PDE-optimized violin plot for either classified or non-classified, univariate or multivariate data. The MD-plot is an alternative for the box-and-whisker diagram (box plot) and bean plot.

ClassViolinPlot: Creates PDE optimized Violin plot for all classes

Description

Usage

Arguments

Value

References