ShepardDensityscatter {DataVisualizations} | R Documentation |
Shepard PDE scatter
Description
Draws ein Shepard Diagram (scatterplot of distances) with an two-dimensional PDE density estimation .
Usage
ShepardDensityScatter(InputDists, OutputDists, Plotter= "native", Type = "DDCAL",
DensityEstimation="SDH", Marginals = FALSE, xlab='Input Distances',
ylab='Output Distances',main='ProjectionMethod', sampleSize=500000)
Arguments
InputDists |
[1:n,1:n] with n cases of data in d variables/features: Matrix containing the distances of the inputspace. |
OutputDists |
[1:n,1:n] with n cases of data in d dimensionalites of the projection method variables/features: Matrix containing the distances of the outputspace. |
Plotter |
Optional, either |
Type |
Optional, either |
DensityEstimation |
Optional, use either |
Marginals |
Optional, either TRUE (draw Marginals) or FALSE (do not draw Marginals) |
xlab |
Label of the x axis in the resulting Plot. |
ylab |
Label of the y axis in the resulting Plot. |
main |
Title of the Shepard diagram |
sampleSize |
Optional, default(500000), reduces a.ount of data for density estimation, if too many distances given |
Details
Introduced and described in [Thrun, 2018, p. 63] with examples in [Thrun, 2018, p. 71-72]
Author(s)
Michael Thrun
References
[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, ISBN: 978-3-658-20540-9, Heidelberg, 2018.
Examples
data("Lsun3D")
Cls=Lsun3D$Cls
Data=Lsun3D$Data
InputDist=as.matrix(dist(Data))
res = stats::cmdscale(d = InputDist, k = 2, eig = TRUE,
add = FALSE, x.ret = FALSE)
ProjectedPoints = as.matrix(res$points)
ShepardDensityScatter(InputDist,as.matrix(dist(ProjectedPoints)),main = 'MDS')
ShepardDensityScatter(InputDist[1:100,1:100],
as.matrix(dist(ProjectedPoints))[1:100,1:100],main = 'MDS')