DataVisualizations (version 1.3.3)

ClassPDEplot: PDE Plot for all classes

Description

PDEplot the data for all classes, weights the pdf with priors

Usage

ClassPDEplot(Data, Cls, ColorSequence,
 ColorSymbSequence, PlotLegend = 1,
 SameKernelsAndRadius = 0, xlim, ylim, ...)

Value

Kernels of the Pareto density estimation in mode invisible

Arguments

Data

The Data to be plotted

Cls

Vector of class identifiers. Can be integers or NaN's, need not be consecutive nor positive

ColorSequence

Optional: the sequence of colors used, Default: DefaultColorSequence

ColorSymbSequence

Optional: the plot symbols used (theoretisch nicht notwendig, da erst wichtig, wenn mehr als 562 Cluster)

PlotLegend

Optional: add a legent to plot (default == 1)

SameKernelsAndRadius

Optional: Use the same PDE kernels and radii for all distributions (default == 0)

xlim

Optional: range of the x axis

ylim

Optional: range of the y axis

...

further arguments passed to plot

Author

Michael Thrun

Examples

# \dontshow{
# sampling is online done
# because otherwise the example takes too long
# in the CRAN check
data(ITS)
ind=sample(length(ITS),1000)

#clustering only for testing
Classification=c(rep(1,8805),rep(2,2389))
DataVisualizations::ClassPDEplot(ITS[ind],Classification[ind])
# }

# \donttest{
data(ITS)
#please download package from cran
#model=AdaptGauss::AdaptGauss(ITS)
#Classification=AdaptGauss::ClassifyByDecisionBoundaries(ITS,

#DecisionBoundaries = AdaptGauss::BayesDecisionBoundaries(model$Means,model$SDs,model$Weights))

DataVisualizations::ClassPDEplot(ITS,Classification)$ggobject
# }