View source: R/SammonsMapping.R
SammonsMapping | R Documentation |
Improved MDS thorugh a normalization of the Input space
SammonsMapping(DataOrDistances,method='euclidean',OutputDimension=2,PlotIt=FALSE,Cls)
DataOrDistances |
Numerical matrix defined as either
or
|
method |
method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary' |
OutputDimension |
Number of dimensions in the Outputspace, default=2 |
PlotIt |
Default: FALSE, If TRUE: Plots the projection as a 2d visualization. |
Cls |
[1:n,1] Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data. |
An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (doi: 10.1007/978-3-658-20540-9).
ProjectedPoints |
[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projectio |
Stress |
Shephard-Kruskal Stress |
A wrapper for sammon
You can use the standard ShepardScatterPlot
or the better approach through the ShepardDensityPlot
of the CRAN package DataVisualizations
.
Michael Thrun
data('Hepta') Data=Hepta$Data Proj=SammonsMapping(Data) ## Not run: PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls) ## End(Not run)
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