stat_pde_density {DataVisualizations} | R Documentation |
Calculate Pareto density estimation for ggplot2 plots
Description
This function enables to replace the default density estimation for ggplot2 plots with the Pareto density estimation [Ultsch, 2005]. It is used for the PDE-Optimized violin plot published in [Thrun et al, 2018].
Usage
stat_pde_density(mapping = NULL,
data = NULL,
geom = "violin",
position = "dodge",
...,
trim = TRUE,
scale = "area",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... |
Other arguments passed on to |
trim |
This parameter only matters if you are displaying multiple densities in one plot. If 'FALSE', the default, each density is computed on the full range of the data. If 'TRUE', each density is computed over the range of that group: this typically means the estimated x values will not line-up, and hence you won't be able to stack density values. |
scale |
When used with geom_violin: if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width. |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
Details
Pareto Density Estimation (PDE) is a method for the estimation of probability density functions using hyperspheres. The Pareto-radius of the hyperspheres is derived from the optimization of information for minimal set size. It is shown, that Pareto Density is the best estimate for clusters of Gaussian structure. The method is shown to be robust when cluster overlap and when the variances differ across clusters.
Author(s)
Felix Pape
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.
[Thrun et al, 2018] Thrun, M. C., Pape, F., & Ultsch, A. : Benchmarking Cluster Analysis Methods using PDE-Optimized Violin Plots, Proc. European Conference on Data Analysis (ECDA), accepted, Paderborn, Germany, 2018.
See Also
[ggplot2]stat_density
Examples
miris <- reshape2::melt(iris)
ggplot2::ggplot(miris,
mapping = ggplot2::aes_string(y = 'value', x = 'variable')) +
ggplot2::geom_violin(stat = "PDEdensity")