DataVisualizations (version 1.1.8)

PDEscatter: Scatter Density Plot

Description

Pareto density estimation (PDE) [Ultsch, 2005] used for a scatter density plot.

Usage

PDEscatter(x, y, na.rm = FALSE, paretoRadius = 0, sampleSize =
                 round(sqrt(5e+08), -3), NrOfContourLines = 20, Plotter
				 
                 = "native", DrawTopView = T, xlab = "X", ylab = "Y",
				 
                 main = "PDEscatter", xlim, ylim, Legendlab_ggplot =
				 
                 "value")

Arguments

x

Numeric vector, first feature (for x axis values)

y

Numeric vector, second feature (for y axis values)

na.rm

Function may not work with non finite values. If these cases should be automatically removed, set parameter TRUE

paretoRadius

Numeric, the Pareto Radius. If omitted (or 0), calculate by paretoRad.

sampleSize

Numeric, maximum size of the sample used for calculation. High values increase runtime significantly.

NrOfContourLines

Numeric, number of contour lines to be drawn. 20 by default.

Plotter

String, name of the plotting backend to use. Possible values are: "native", "ggplot", "plotly"

DrawTopView

Boolean, True means contur is drawn, otherwise a 3D plot is drawn. Default: True

xlab

String, title of the x axis. Default: "X", see plot() function

ylab

String, title of the y axis. Default: "Y", see plot() function

main

string, the same as "main" in plot() function

xlim

see plot() function

ylim

see plot() function

Legendlab_ggplot

String, in case of Plotter="ggplot" label for the legend. Default: "value"

Value

List of:

AnzInPSpheres

Number of points within the ParetoRadius of each point

ParetoRadius

ParetoRadius used for PDEscatter

Handle

Handle of the plot object. Information-string if native R plot is used.

Details

The PDEscatter function generates the density of the xy data as a z coordinate. Afterwards xyz will be plotted either as a contour plot or a 3d plot. This function plots the PDE on top of a scatterplot. Variances of x and y should not differ by extreme numbers, otherwise calculate the percentiles on both first. The method was succesfully used in [Thrun, 2018; Thrun/Ultsch 2018].

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, (Ultsch, A. & Huellermeier, E. Eds., 10.1007/978-3-658-20540-9), Doctoral dissertation, Heidelberg, Springer, ISBN: 978-3658205393, 2018.

[Thrun/Ultsch, 2018] Thrun, M. C., & Ultsch, A. : Effects of the payout system of income taxes to municipalities in Germany, in Papiez, M. & Smiech,, S. (eds.), Proc. 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, pp. 533-542, Cracow: Foundation of the Cracow University of Economics, Cracow, Poland, 2018.

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, In Baier, D. & Werrnecke, K. D. (Eds.), Innovations in classification, data science, and information systems, (Vol. 27, pp. 91-100), Berlin, Germany, Springer, 2005.

Examples

# NOT RUN {
#taken from [Thrun/Ultsch, 2018]
data("ITS")
data("MTY")
Inds=which(ITS<900&MTY<8000)
plot(ITS[Inds],MTY[Inds],main='Bimodality is not visible in normal scatter plot')
# }
# NOT RUN {
PDEscatter(ITS[Inds],MTY[Inds],xlab = 'ITS in EUR',

ylab ='MTY in EUR' ,main='Pareto Density Estimation indicates Bimodality' )
# }