DensityContour {DataVisualizations}

Contour plot of densities

Description

Density estimation (PDE) [Ultsch, 2005] or "SDH" [Eilers/Goeman, 2004] used for a density contour plot.

Usage

DensityContour(X,Y, DensityEstimation="SDH", 

SampleSize, na.rm=FALSE,PlotIt=TRUE,
                              
NrOfContourLines=20,Plotter='ggplot', DrawTopView = TRUE,
                              
xlab, ylab, main="DensityContour",
                              
xlim, ylim, Legendlab_ggplot="value",

AddString2lab="",NoBinsOrPareto=NULL,...)

Arguments

X

Numeric vector [1:n], first feature (for x axis values)

Y

Numeric vector [1:n], second feature (for y axis values)

DensityEstimation

"SDH" is very fast but maybe not correct, "PDE" is slow but proably more correct, third alternativ is the typical R density estimation with "kde2d" which is sensitive to parameters

SampleSize

Numeric, positiv scalar, maximum size of the sample used for calculation. High values increase runtime significantly. The default is that no sample is drawn

na.rm

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

PlotIt

TRUE: plots with function call

FALSE: Does not plot, plotting can be done using the list element Handle

NrOfContourLines

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

Plotter

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

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"

AddString2lab

adds the same string of information to x and y axis label, e.g. usefull for adding SI units

NoBinsOrPareto

Density specifc parameters, for PDEscatter(ParetoRadius) or SDH (nbins)) or kde2d(bins)

...

further plot arguments

Details

The DensityContour 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. It assumens that the cases of x and y are mapped to each other meaning that a cbind(x,y) operation is allowed. This function plots the Density on top of a scatterplot. Variances of x and y should not differ by extreme numbers, otherwise calculate the percentiles on both first. If DrawTopView=FALSE only the plotly option is currently available. If another option is chosen, the method switches automatically there.

PlotIt=FALSE is usefull if one likes to perform adjustements like axis scaling prior to plotting with ggplot2 or plotly.

Value

List of:

X

Numeric vector [1:m],m<=n, first feature used in the plot or the kernels used

Y

Numeric vector [1:m],m<=n, second feature used in the plot or the kernels used

Densities

Number of points within the ParetoRadius of each point, i.e. density information

Handle

Handle of the plot object

Note

MT contributed with several adjustments

Author(s)

Felix Pape

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.

[Eilers/Goeman, 2004] Eilers, P. H., & Goeman, J. J.: Enhancing scatterplots with smoothed densities, Bioinformatics, Vol. 20(5), pp. 623-628. 2004.

Examples

#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')

DensityContour(ITS[Inds],MTY[Inds],DensityEstimation="SDH",xlab = 'ITS in EUR',

ylab ='MTY in EUR' ,main='Smoothed Densities histogram indicates Bimodality' )

DensityContour(ITS[Inds],MTY[Inds],DensityEstimation="PDE",xlab = 'ITS in EUR',

ylab ='MTY in EUR' ,main='PDE indicates Bimodality' )