PDEplot {DataVisualizations}

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)
				 

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

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.

Author(s)

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

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