DualaxisClassplot

Allows to plot two time series or features with one or two classification(a) as labeled scatter plots.
The colors are the labels defined by the classification.
Usefull to see if temporal clustering has time dependent variations and for Hidden Markov Models (see Mthrun/RHmm on GitHub).

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

Craig Varrichio

Alfred Ultsch

DualaxisClassplot function

<dl> <dt>X</dt>
<dd>[1:n] numeric vector or time</dd> <dt>Y1</dt>
<dd>[1:n] numeric vector of feauture</dd> <dt>Y2</dt>
<dd>[1:n] numeric vector of feauture</dd> <dt>Cls1</dt>
<dd>[1:n] numeric vector defining a classification of k1 classes</dd> <dt>Cls2</dt>
<dd>Optional, [1:n] numeric vector defining a classification of k2 classes for <code>Y2</code></dd> <dt>xlab</dt>
<dd>Optional, string</dd> <dt>y1lab</dt>
<dd>Optional, string</dd> <dt>y2lab</dt>
<dd>Optional, string</dd> <dt>main</dt>
<dd>Optional, string</dd> <dt>Colors</dt>
<dd>[1:(k1+k2)] Colornames</dd> <dt>Showgrid</dt>
<dd>Optional, boolean</dd> <dt>SaveIt</dt>
<dd>Optional, boolean</dd></dl>

Arguments

Author

Dualaxis Classplot — DualaxisClassplot

<dl>

 <dt>X</dt>
<dd>[1:n] numeric vector or time</dd>

 <dt>Y1</dt>
<dd>[1:n] numeric vector of feauture</dd>

 <dt>Y2</dt>
<dd>[1:n] numeric vector of feauture</dd>

 <dt>Cls1</dt>
<dd>[1:n] numeric vector defining a classification of k1 classes</dd>

 <dt>Cls2</dt>
<dd>Optional, [1:n] numeric vector defining a classification of k2 classes for <code>Y2</code></dd>

 <dt>xlab</dt>
<dd>Optional, string</dd>

 <dt>y1lab</dt>
<dd>Optional, string</dd>

 <dt>y2lab</dt>
<dd>Optional, string</dd>

 <dt>main</dt>
<dd>Optional, string</dd>

 <dt>Colors</dt>
<dd>[1:(k1+k2)] Colornames</dd>

 <dt>Showgrid</dt>
<dd>Optional, boolean</dd>

 <dt>SaveIt</dt>
<dd>Optional, boolean</dd>

</dl>

Dualaxis Classplot

DualaxisClassplot: Dualaxis Classplot

Description

Usage

Value

Arguments

Author

See Also

Examples