| ClassPDEplot {DataVisualizations} | R Documentation |
PDEplot the data for all classes, weights the pdf with priors
ClassPDEplot(Data, Cls, ColorSequence, ColorSymbSequence, PlotLegend = 1, SameKernelsAndRadius = 0, xlim, ylim, ...)
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 |
Kernels of the Pareto density estimation in mode invisible
Michael Thrun
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