View source: R/TopviewTopographicMap.R
TopviewTopographicMap | R Documentation |
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.
TopviewTopographicMap(GeneralizedUmatrix, BestMatchingUnits, Cls, ClsColors = NULL, Imx = NULL, ClsNames = NULL, BmSize = 6, DotLineWidth = 2, alpha = 1, ...)
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 |
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:
|
BmSize |
size(diameter) of the points in the visualizations. The points represent the BestMatchingUnits |
DotLineWidth |
... |
alpha |
... |
... |
|
Please see plotTopographicMap
. This function is currently still experimental because not all functionallity is fully tested yet.
plotly handler
Names are currently under development, Imx in testing phase.
Tim Schreier, Luis Winckelmann, Michael Thrun
[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.
plotTopographicMap
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.