ParetoRadius: ParetoRadius for distributions

In Mthrun/DataVisualizations: Visualizations of High-Dimensional Data

ParetoRadius for distributions

Description

Calculation of the ParetoRadius i.e. the 18 percentiles of all mutual Euclidian distances in data.

Usage

ParetoRadius(Data, maximumNrSamples = 10000,
  plotDistancePercentiles = FALSE,Compute="Cpp")

Arguments

Data	numeric data vector
maximumNrSamples	Optional, numeric. Maximum number for which the distance calculation can be done. 1000 by default.
plotDistancePercentiles	Optional, logical. If TRUE, a plot of the percentiles of distances is produced. FALSE by default.
Compute	Either Cpp or Cpp_exp defines which subalgorithms are used to compute pareto radus. Cpp_exp is faster but not verified yet.

Details

The Pareto-radius of the hyperspheres is derived from the optimization of information for minimal set size. ParetoRadius() is a kernel density estimation for variable intervals. It works only on Data without missing values (NA) or NaN. In other cases, please use ParetoDensityEstimation directly.

Value

numeric value, the Pareto radius.

Note

This the second version of the function prior available in AdaptGauss.

For larger datasets the quantile_c() function is used instead of quantile in R which was programmed by Dirk Eddelbuettel on Jun 6 and taken by the author from https://github.com/RcppCore/Rcpp/issues/967.

Author(s)

Michael Thrun

References

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, Proc Gfkl 2003, pp 91-100, Springer, Berlin, 2005.

See Also

ParetoDensityEstimation, OptimalNoBins

Mthrun/DataVisualizations documentation built on Feb. 21, 2025, 1:36 a.m.