PDEplot: PDE plot

Description

This function plots the Pareto probability density estimation (PDE), uses PDEstimationForGauss and ParetoRadius.

Usage

PDEplot(Data, paretoRadius = 0, weight = 1, kernels = NULL,
                 LogPlot = F, PlotIt = TRUE, title =
                 "ParetoDensityEstimation(PDE)", color = "blue",
				 
                 xpoints = FALSE, xlim, ylim, xlab, ylab =
                 "PDE", ggPlot = ggplot(), sampleSize = 2e+05, lwd = 2)

Value

kernels: numeric vector. The x points of the PDE function.
paretoDensity: numeric vector, the PDE(x).
paretoRadius: numeric value, the Pareto Radius used for the plot.
ggPlot: ggplot2 object. Can be used to further modify the plot or add other plots.

Arguments

Data: [1:n] numeric vector of data to be plotted.
paretoRadius: numeric, the Pareto Radius. If omitted, calculate by paretoRad.
weight: numeric, Weight*ParetoDensity is plotted. 1 by default.
kernels: numeric vector of kernels. Optional
LogPlot: LogLog PDEplot if TRUE, xpoints has to be FALSE. Optional
PlotIt: logical, if plot. TRUE by default.
title: character vector, title of plot.
color: character vector, color of plot.
xpoints: logical, if TRUE only points are plotted. FALSE by default.
xlim: Arguments to be passed to the plot method.
ylim: Arguments to be passed to the plot method.
xlab: Arguments to be passed to the plot method.
ylab: Arguments to be passed to the plot method.
ggPlot: ggplot2 object to be plotted upon. Insert an exisiting plot to add a new PDEPlot to it. Default: empty plot
sampleSize: default(200000), sample size, if datavector is to big
lwd: linewidth, see plot

Author

Michael Thrun

References

Ultsch, A.: Pareto Density Estimation: A Density Estimation for Knowledge Discovery, Baier D., Wernecke K.D. (Eds), In Innovations in Classification, Data Science, and Information Systems - Proceedings 27th Annual Conference of the German Classification Society (GfKL) 2003, Berlin, Heidelberg, Springer, pp, 91-100, 2005.

Examples

Run this code

x <- rnorm(1000, mean = 0.5, sd = 0.5)
y <- rnorm(750, mean = -0.5, sd = 0.75)
plt <- PDEplot(x, color = "red")$ggPlot
plt <- PDEplot(y, color = "blue", ggPlot = plt)$ggPlot

# Second Example
#  ggplotObj=ggplot()
#  for(i in 1:length(Variables))
#     ggplotObj=PDEplot(Data[,i],ggPlot = ggplotObj)$ggPlot

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