Calculate Pareto density estimation for ggplot2 plots
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].
stat_pde_density(mapping = NULL, data = NULL, geom = "violin", bounds = bounds, position = "dodge", ..., trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
mapping: Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
data: The data to be displayed in this layer. There are three options:
If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().
A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.
A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data.
geom: The geometric object to use display the data
bounds: bounds
position: Position adjustment, either as a string, or the result of a call to a position adjustment function.
...: Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.
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 FALSE (the default), removes missing values with a warning. If TRUE, silently removes missing values.
show.legend: logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
inherit.aes: If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().
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.
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.
Felix Pape
stat_density
miris <- reshape2::melt(iris) ggplot2::ggplot(miris, mapping = ggplot2::aes(y = .data$value, x = .data$variable)) + ggplot2::geom_violin(stat = "PDEdensity")