DataVisualizations (version 1.3.3)

ClassMDplot: Class MDplot for Data w.r.t. all classes

Description

Creates a Mirrored-Density plot w.r.t. to each class of a numerical vector of data.

Usage

ClassMDplot(Data, Cls, ColorSequence = DataVisualizations::DefaultColorSequence,
                         ClassNames = NULL, PlotLegend = TRUE,Ordering = "Columnwise",
                         
                         main = 'MDplot for each Class',
                         
                         xlab = 'Classes', ylab = 'PDE of Data per Class',
                         
                         Fill = 'darkblue', MinimalAmoutOfData=40,
                         
                         MinimalAmoutOfUniqueData=12,SampleSize=1e+05,...)

Value

A List of

ClassData

The matrix [1:m,1:NoOfClasses] used to plot with the reordered Cls, rows are filled partly with NaN, m is the length of the number of data in largest class.

ggobject

The ggplot2 plot object

in mode invisible

Arguments

Data

[1:n] Vector of the data to be plotted

Cls

[1:n] Vector of class identifiers of k clusters one number is the label of one cluster

ColorSequence

Optional: [1:k] vector, The sequence of colors used, Default: DataVisualizations::DefaultColorSequence

ClassNames

Optional: [1:k] named numerical vector, The names of the classes. Default: Class 1 - Class k with k beeing the number of classes

PlotLegend

Optional: Add a legent to plot. Default: TRUE)

Ordering

Optional: Ordering of Classes, please see MDplot for details)

main

Optional: Title of the plot. Default: MDplot for each Class

Fill

Optional: [1:k] Vector with the colors, the MD's are to be colored with. If only one value is given, all MD's are colored in the same color.

xlab

Optional: Title of the x axis. Default: "Classes"

ylab

Optional: Title of the y axis. Default: "Data"

MinimalAmoutOfData

Optional: numeric value defining a threshold. Below this threshold no density estimation is performed and a Jitter plot with a median line is drawn. Please see MDplot for details.

MinimalAmoutOfUniqueData

Optional: numeric value defining a threshold. Below this threshold no density estimation and statistical testing is performed and a Jitter plot is drawn. Only Data Science experts should change this value after they understand how the density is estimated (see [Ultsch, 2005]).

SampleSize

Optional: numeric value defining a threshold. Above this thresholdclass-wise uniform sampling of finite cases is performed in order to shorten computation time. If required, SampleSize=n can be set to omit this procedure.

...

Further arguments that are documented in MDplot except for OnlyPlotOutput which is always true.

Author

Michael Thrun, Felix Pape

Details

Further examples for the ClassMDplot can be found in https://md-plot.readthedocs.io/en/latest/application/example_application.html.

The Cls vector is reordered from lowest to highest number. The ClassNames vector and ColorSequence vectors are matched by this ordering of Cls, i.e. the lowest number gets the first color or class name.

References

Thrun, M. C., Breuer, L., & Ultsch, A. : Knowledge discovery from low-frequency stream nitrate concentrations: hydrology and biology contributions, Proc. European Conference on Data Analysis (ECDA), Paderborn, Germany, 2018.

See Also

https://md-plot.readthedocs.io/en/latest/application/example_application.html

MDplot https://pypi.org/project/md-plot/

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::ClassMDplot(ITS[ind],Classification[ind])
# }

# \donttest{
data(ITS)

#shortcut for example if AdaptGauss not installed
Classification = kmeans(ITS, centers = 2)$cluster

#better approach
#please download package from cran
#model=AdaptGauss::AdaptGauss(ITS)
#Classification=AdaptGauss::ClassifyByDecisionBoundaries(ITS,

#DecisionBoundaries = AdaptGauss::BayesDecisionBoundaries(model$Means,model$SDs,model$Weights))
ClassNames=c(1,2)
names(ClassNames)=c("Insert name \n of Class 1","Insert name \n  of Class 2")
ClassMDplot(ITS,Classification,ClassNames = ClassNames)
# }