R: Shepard PDE scatter

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',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.
`xlab` | Label of the x axis in the resulting Plot.
`ylab` | Label of the y axis in the resulting Plot.
`Plotter` | see `PDEscatter` for details
`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')

[Package DataVisualizations version 1.2.3 Index]