R: Create PDE plot for all classes with maximum likelihood

ClassPDEplotMaxLikeli {DataVisualizations} | R Documentation

## 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

### Author(s)

Felix Pape

### 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

    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

[Package DataVisualizations version 1.2.3 Index]