DataVisualizations (version 1.1.12)

DataVisualizations-package: DataVisualizations

Description

DataVisualizations

Details

For a brief introduction to DataVisualizations please see the vignette A Quick Tour in Data Visualizations.

Please see http://www.deepbionics.org/. Depending on the context please cite either [Thrun, 2018] regarding visualizations in the context of clustering or [Thrun/Ultsch, 2018] for other visualizations.

For the Mirrored Density Plot (MD plot) please cite [Thrun et al., 2019] and see the extensive vignette in https://md-plot.readthedocs.io/en/latest/index.html. The MD plot is also available in Python https://pypi.org/project/md-plot/

DataVisualizations

References

[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, https://doi.org/10.1007/978-3-658-20540-9, 2018.

[Thrun/Ultsch, 2018] Thrun, M. C., & Ultsch, A. : Effects of the payout system of income taxes to municipalities in Germany, in Papiez, M. & Smiech,, S. (eds.), Proc. 12th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena, pp. 533-542, Cracow: Foundation of the Cracow University of Economics, Cracow, Poland, 2018.

[Thrun et al., 2019] Thrun, M. C., Gehlert, Tino, & Ultsch, A. : Analyzing the Fine Structure of Distributions, arXiv:1908.06081, 2019.

Examples

# NOT RUN {

data("Lsun3D")
Data=Lsun3D$Data
# }
# NOT RUN {
Pixelmatrix(Data)
# }
# NOT RUN {
# }
# NOT RUN {
InspectDistances(as.matrix(dist(Data)))
# }
# NOT RUN {
data("ITS")
data("MTY")
Inds=which(ITS<900&MTY<8000)
plot(ITS[Inds],MTY[Inds],main='Bimodality is not visible in normal scatter plot')
# }
# NOT RUN {
PDEscatter(ITS[Inds],MTY[Inds],xlab = 'ITS in EUR',

ylab ='MTY in EUR' ,main='Pareto Density Estimation indicates Bimodality' )
# }
# NOT RUN {
MAlist=MAplot(ITS,MTY)

data("Lsun3D")
Cls=Lsun3D$Cls
Data=Lsun3D$Data
#clear cluster structure
plot(Data[,1:2],col=Cls)
#However, the silhouette plot does not indicate a very good clustering in cluster 1 and 2
# }
# NOT RUN {
Silhouetteplot(Data,Cls = Cls)
# }
# NOT RUN {
Heatmap(as.matrix(dist(Data)),Cls = Cls)
# }