TopviewTopographicMap: Topview of Topographic Map ind 2D

In GeneralizedUmatrix: Credible Visualization for Two-Dimensional Projections of Data

Topview of Topographic Map ind 2D

Description

Fast Visualization of the Generalized U-matrix in 2D which visualizes high-dimensional distance and density based structurs of the combination two-dimensional scatter plots (projections) with high-dimensional data.

Usage

TopviewTopographicMap(GeneralizedUmatrix, BestMatchingUnits,

Cls, ClsColors = NULL, Imx = NULL,

ClsNames = NULL, BmSize = 6, DotLineWidth = 2,

alpha = 1, ...)

Arguments

GeneralizedUmatrix	[1:Lines,1:Columns] Umatrix to be plotted, numerical matrix storing the U-heights, see [Thrun, 2018] for definition.
BestMatchingUnits	[1:n,1:2], Positions of bestmatches to be plotted onto the Umatrix
Cls	[1:n], numerical vector of classification of k classes for the bestmatch at the given point
ClsColors	Vector of colors that will be used to colorize the different classes
Imx	a mask (Imx) that will be used to cut out the umatrix
ClsNames	If set: [1:k] character vector naming the k classes for the legend. In this case, further parameters with the possibility to adjust are: LegendCex: (size); NamesOrientation: Legend position "v" or "h"; NamesTitle: title of legend.
BmSize	size(diameter) of the points in the visualizations. The points represent the BestMatchingUnits
DotLineWidth	...
alpha	...
...	Tiled Should the Umatrix be drawn 4times? main set specific title in plot ExtendBorders scalar, extends Umatrix by toroidal continuation of the given Umatrix MainCex scalar, magnification to be used for legend LegendCex scalar, magnification to be used for main titles _ Further Arguments relevant for interactive shiny application

Details

Please see plotTopographicMap. This function is currently still experimental because not all functionallity is fully tested yet.

Value

plotly handler

Note

Names are currently under development, Imx in testing phase.

Author(s)

Tim Schreier, Luis Winckelmann, Michael Thrun

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, doi: 10.1007/978-3-658-20540-9, 2018.

[Thrun et al., 2016] Thrun, M. C., Lerch, F., Loetsch, J., & Ultsch, A.: Visualization and 3D Printing of Multivariate Data of Biomarkers, in Skala, V. (Ed.), International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Vol. 24, Plzen, http://wscg.zcu.cz/wscg2016/short/A43-full.pdf, 2016.

See Also

plotTopographicMap

Examples

data("Chainlink")
Data=Chainlink$Data
Cls=Chainlink$Cls
InputDistances=as.matrix(dist(Data))
res=cmdscale(d=InputDistances, k = 2, eig = TRUE, add = FALSE, x.ret = FALSE)
ProjectedPoints=as.matrix(res$points)
#see also ProjectionBasedClustering package for other common projection methods

resUmatrix=GeneralizedUmatrix(Data,ProjectedPoints)
## visualization
TopviewTopographicMap(GeneralizedUmatrix = resUmatrix$Umatrix,resUmatrix$Bestmatches)

GeneralizedUmatrix documentation built on Nov. 28, 2022, 5:13 p.m.