DataVisualizations (version 1.3.3)

estimateDensity2D: 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)

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

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)

Author

Luca Brinkman and Michael Thrun

Details

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

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)