PDEstrip

This function renders a single variable's probability density as a horizontal "heat strip", 
where color encodes local density values. Low-density regions are shown in blue/green, 
medium-density regions in yellow, and high-density regions in orange/red. 
It provides a compact alternative to violin or box plots for visualizing the distribution 
of a single variable.

density

visualization

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data 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 a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Michael Thrun

DataVisualizations

Visualizations of High-Dimensional Data

Felix Pape

Onno Hansen-Goos

Quirin Stier

Hamza Tayyab

Luca Brinkmann

Dirk Eddelbuettel

Winston Chang

Craig Varrichio

Alfred Ultsch

PDEstrip function

<dl> <dt>Feature</dt>
<dd>Numeric vector. Finite values are used for Pareto Density Estimation (PDE). 
 Non-finite values are ignored.</dd> <dt>palette</dt>
<dd>Character vector of colors defining the low-to-high density gradient, 
 passed to <code><a href="https://ggplot2.tidyverse.org/reference/scale_gradient.html">ggplot2::scale_fill_gradientn()</a></code>. 
 Default is a five-color palette ranging from blue (low density) to red (high density).</dd></dl>

Arguments

Author

1D Density Strip based on Pareto Density Estimation (PDE) — PDEstrip

<dl>

 <dt>Feature</dt>
<dd>Numeric vector. Finite values are used for Pareto Density Estimation (PDE). 
 Non-finite values are ignored.</dd>

 <dt>palette</dt>
<dd>Character vector of colors defining the low-to-high density gradient, 
 passed to <code><a href='https://ggplot2.tidyverse.org/reference/scale_gradient.html'>ggplot2::scale_fill_gradientn()</a></code>. 
 Default is a five-color palette ranging from blue (low density) to red (high density).</dd>

</dl>

1D Density Strip based on Pareto Density Estimation (PDE)

PDEstrip: 1D Density Strip based on Pareto Density Estimation (PDE)

Description

Usage

Value

Arguments

Author

Details

References

Examples