estimateDensity2D: estimateDensity2D

In DataVisualizations: Visualizations of High-Dimensional Data

estimateDensity2D

Description

Estimates densities for two-dimensional data with the given estimation type

Usage

estimateDensity2D(X, Y, DensityEstimation = "SDH",

SampleSize, na.rm = FALSE, NoBinsOrPareto = NULL)

Arguments

X	[1:n] numerical vector of first feature
Y	[1:n] numerical vector of second feature
DensityEstimation	Either "PDE","SDH" or "kde2d"
SampleSize	Sample Size in case of big data
na.rm	Function may not work with non finite values. If these cases should be automatically removed, set parameter TRUE
NoBinsOrPareto	Density specifc parameters, for PDEscatter(ParetoRadius) or SDH (nbins)) or kde2d(bins)

Details

Each two-dimensional data point is defined by its corresponding X and Y value.

Value

List V with

X	[1:m] numerical vector of first feature, m<=n depending if all values are finite an na.rm parameter
Y	[1:m] numerical vector of second feature, m<=n depending if all values are finite an na.rm parameter
Densities	the density of each two-dimensional data point

Author(s)

Luca Brinkman and Michael Thrun

References

[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

X=runif(100)
Y=rnorm(100)
#V=estimateDensity2D(X,Y)

DataVisualizations documentation built on Oct. 10, 2023, 9:06 a.m.