Draws a plot based of given Umatrix or Pmatrix.
A 'ggplot' of a Matrix
Umatrix or Pmatrix to be plotted
Positions of BestmMtches to be plotted onto the Umatrix
Class identifier for the BestMatches
Vector of colors that will be used to colorize the different classes
If "Umatrix" the colors of a Umatrix (Blue -> Green -> Brown -> White) will be used; If "Pmatrix" the colors of a Pmatrix (White -> Yellow -> Red) will be used
Should the Umatrix be drawn 4times?
Integer between 0.1 and 5, magnification factor of the drawn BestMatch circles
If TRUE, a color legend will be drawn next to the plot
If TRUE, the plot will be drawn with a fixed ratio of x and y axis
Only draws the area within given polygon
Number of height levels that will be used within the Umatrix
Use half transparent contours. Looks better but is slow
Mask to cut out an island. Every value should be either 1 (stays in) or 0 (gets cut out)
If TRUE axis, margins, ... surrounding the Umatrix image will be removed
If TRUE, the surrounding blue area around an island will be reduced as much as possible (while still maintaining a rectangular form)
If TRUE, the surrounding blue area around an island will be transparent
A title that will be drawn above the plot
Vector of strings corresponding to the order of BestMatches which will be drawn on the plot as labels
Numeric value of Shape that will be used. Responds to the usual shapes of ggplot
If TRUE, BestMatches that are shown more than once within an island, will be marked
If TRUE, a yellow circle is drawn around Bestmatches to distinct them better from background
The heightScale (nrlevels) is set at the proportion of the 1 percent quantile against the 99 percent quantile of the matrix values.
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,Plzen, 2016.
Ultsch, A.: Maps for the visualization of high-dimensional data spaces, Proc. Workshop on Self organizing Maps (WSOM), pp. 225-230, Kyushu, Japan, 2003.
Siemon, H.P., Ultsch,A.: Kohonen Networks on Transputers: Implementation and Animation, in: Proceedings Intern. Neural Networks, Kluwer Academic Press, Paris, pp. 643-646, 1990.
data("Hepta")
e = esomTrain(Hepta$Data, Key = 1:nrow(Hepta$Data))
plotMatrix(e$Umatrix,e$BestMatches)
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