DataVisualizations (version 1.3.3)

PDEnormrobust: PDEnormrobust

Description

This functions plots ParetoDensityEsrtimation (PDE) and robustly estimated Gaussian with empirical Mean and Variance

Usage

PDEnormrobust(Data,xlab='PDE',ylab,main='PDEnormrobust',
                          PlotSymbolPDE='blue',
                          PlotSymbolGauss= 'magenta',PlotIt=TRUE,
                          Mark2Sigma=FALSE,Mark3Sigma=FALSE,
                          p_mean=10,p_sd=25,...)

Value

Kernels

numeric vector. The x points of the PDE function.

ParetoDensity

estimated pdf of data, numeric vector, the PDE(x).

ParetoRadius

numeric value, the Pareto Radius used for the plot.

Normaldist

pdf based on rubstly estimated parameters

Pars

Named vector of robustly estimatated Mean, standard deviation SD, Sigma2=1.96*SD and Sigma3=2.576*SD, EstPercData_Sigma2, EstPercData_Sigma3

Arguments

Data

numeric vector, data to be plotted.

xlab

Optional,see plot

ylab

Optional,see plot

main

Optional,see plot

PlotSymbolPDE

line color pdf

PlotSymbolGauss

line color robust gauss

PlotIt

TRUE: shows plot

Mark2Sigma

TRUE: sets to vertical lines marking data outside M+-1.96SD

Mark3Sigma

TRUE: sets to vertical lines marking data outside M+-2.576SD

p_mean

scalar between 1-99, percent of the top- and bottomcut from x

p_sd

scalar between 1-99, lowInnerPercentile for robustly estimated standard deviation

...

Further arguments for plot

Author

Michael Thrun

Details

Within Mark2Sigma 95 percent of data should be contained if distribution is Gaussian

Within Mark3Sigma 99 percent of data should be contained if distribution is Gaussian

The 3sgima rule is usually defined as M+-3SD containing 99.7 percent of data but to simplify, the input parameter name is called Mark3Sigma instead Mark2comma576Sigma, the same reason applies to the output parameter Sigma3.

Examples

data(MTY)
# \donttest{
PDEnormrobust(unname(MTY))
# }