MDplot4multiplevectors() R function from [DataVisualizations]

Mirrored Density plot (MD-plot)for Multiple Vectors

This function creates a MD-plot for multiple numerical vectors of various lenghts. The MD-plot is a visualization for a boxplot-like Shape of the PDF published in [Thrun et al., 2020]. It is an improvement of violin or so-called bean plots and posses advantages in comparison to the conventional well-known box plot [Thrun et al., 2020].


MDplot4multiplevectors(..., Names, Ordering = 'Columnwise',
Scaling = "None", Fill = 'darkblue', RobustGaussian = TRUE,

GaussianColor = 'magenta', Gaussian_lwd = 1.5, BoxPlot = FALSE,

BoxColor = 'darkred', MDscaling = 'width', LineSize = 0.01,

LineColor = 'black', QuantityThreshold = 40, UniqueValuesThreshold = 12,

SampleSize = 5e+05, SizeOfJitteredPoints = 1, OnlyPlotOutput = TRUE)

Arguments

...: Either d numerical vectors of different lengths or a list of length d where each element of the list is an vector of arbitrary length
Names: Optional: [1:d] Names of the variables. If missing, the columnnames of data are used.
Ordering: Optional: string, either Default, Columnwise, Alphabetical or Statistics. Please see details for explanation.
Scaling: Optional, Default is None, Percentalize, CompleteRobust, Robust or Log, Please see details for explanation.
Fill: Optional: string, color with which MDs are to be filled with.
RobustGaussian: Optional: If TRUE: each MDplot of a variable is overlayed with a roubustly estimated unimodal Gaussian distribution in the range of this variable, if statistical testing does not yield a significant p.value. In this case the packages moments, diptest and signal are required.
GaussianColor: Optional: string, color of robustly estimated gaussian, only for RobustGaussian=TRUE.
Gaussian_lwd: Optional: numerical, line width of robustly estimated gaussian, only for RobustGaussian=TRUE.
BoxPlot: Optional: If TRUE: each MDplot is overlayed with a Box-Whisker Diagram.
BoxColor: Optional: string, color of Boxplot, only for BoxPlot=TRUE.
MDscaling: Optional: if "area", all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width" (default), all MDs have the same maximum width.
LineSize: Optional: numerical, linewidth of line around the mirrored densities.
LineColor: Optional: string, color of line around the mirrored densities. NA disables this features which is usefull if ones wants to avoid vertical lines leading to outliers.
QuantityThreshold: Optional: numeric value defining a threshold. Below this threshold no density estimation is performed and a jitter plot with a median line is drawn. Only Data Science experts should change this value after they understand how the density is estimated (see [Ultsch, 2005]).
UniqueValuesThreshold: 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 threshold uniform sampling of finite cases is performed in order to shorten computation time.If rowr is not installed, uniform sampling of all cases is performed. If required, SampleSize=n can be set to omit this procedure.
SizeOfJitteredPoints: Optional: scalar. If Not enough unique values for density estimation are given, data points are jittered. This parameter defines the size of the points.
OnlyPlotOutput: Optional: Default TRUE only a ggplot object is given back, if FALSE: Additinally Scaled Data and ordering are the output of this function in a list.

Returns

In the default case of OnlyPlotOutput==TRUE: The ggplot object of the MD-plot.

Otherwise for OnlyPlotOutput==FALSE: A list of - ggplotObj: The ggplot object of the MD-plot.

Ordering: The ordering of columns of data defined by Ordering.
DataOrdered: [1:n,1:d] matrix of ordered and scaled data defined by Ordering and Scaling.

Note that the package ggExtra is not necessarily required but if given the feauture names are automatically rotated.

Author(s)

Michael Thrun.

Examples


MDplot4multiplevectors(runif(20000, 1, 5),c(rnorm(20000,0,1),

rnorm(20000,2.6,1)),c(rnorm(2000,2.5,1)),rpois(25000,5),

Names=c('A','B','C','D'))

V=list(runif(20000, 1, 5),c(rnorm(20000,0,1),

rnorm(20000,2.6,1)),c(rnorm(2000,2.5,1)),rpois(25000,5))

MDplot4multiplevectors(V,Names=c('A','B','C','D'))

Note

cbind.fill is internally used from the depricated R package rowr of Craig Varrichio.

Details

Please see MDplot for details.

References

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, in Baier, D.; Werrnecke, K. D., (Eds), Innovations in classification, data science, and information systems, Proc Gfkl 2003, pp 91-100, Springer, Berlin, 2005.

[Thrun et al., 2020] Thrun, M. C., Gehlert, T. & Ultsch, A.: Analyzing the Fine Structure of Distributions, PLoS ONE, Vol. 15(10), pp. 1-66, DOI 10.1371/journal.pone.0238835, 2020.

MDplot4multiplevectors function