TopviewTopographicMap {GeneralizedUmatrix}

Top view of the topographic map in 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] U-matrix 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 U-matrix

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 U-matrix

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 U-matrix be drawn 4times?

main

set specific title in plot

ExtendBorders

scalar, extends U-matrix by toroidal continuation of the given U-matrix

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)