DataVisualizations (version 1.1.12)

ClassPDEplotMaxLikeli: Create PDE plot for all classes with maximum likelihood

Description

PDEplot the data for allclasses, weight the Plot with 1 (= maximum likelihood)

Usage

ClassPDEplotMaxLikeli(Data, Cls, ColorSequence = DataVisualizations::DefaultColorSequence,
 ClassNames, PlotLegend = TRUE, MinAnzKernels = 0,PlotNorm,
 main = "Pareto Density Estimation (PDE)",
 xlab = "Data", ylab = "ParetoDensity", xlim, ylim, lwd=1, …)

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

ClassNames

Optional: the names of the classes to be displayed in the legend

PlotLegend

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

MinAnzKernels

Optional: Minimum number of kernels

PlotNorm

Optional: ==1 => plot Normal distribuion on top , ==2 = plot robust normal distribution,; default: PlotNorm= 0

main

Optional: Title of the plot

xlab

Optional: title of the x axis

ylab

Optional: title of the y axis

xlim

Optional: area of the x-axis to be plotted

lwd

Optional: area of the y-axis to be plotted

ylim

numerical scalar defining the width of the lines

…

further arguments passed to plot

Value

Kernels

Kernels of the distributions

ClassParetoDensities

Pareto densities for classes

ggobject

ggplot2 plot object. This should be used to further modify the plot

References

Aubert, A. H., Thrun, M. C., Breuer, L., & Ultsch, A. : Knowledge discovery from high-frequency stream nitrate concentrations: hydrology and biology contributions, Scientific reports, Nature, Vol. 6(31536), pp. doi 10.1038/srep31536, 2016.

Examples

# NOT RUN {
# }
# NOT RUN {
data(ITS)
#model=AdaptGauss::AdaptGauss(ITS)
##please download package from cran
#Classification=AdaptGauss::ClassifyByDecisionBoundaries(ITS,

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

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