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Construct a principal surface that are two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. For more information on principal surfaces, see Ganey, R. (2019, "https://open.uct.ac.za/items/4e655d7d-d10c-481b-9ccc-801903aebfc8").
Version: | 1.0 |
Imports: | rgl |
Suggests: | stats, Matrix, akima, knitr, rmarkdown |
Published: | 2025-03-12 |
DOI: | 10.32614/CRAN.package.prinsurf |
Author: | Raeesa Ganey |
Maintainer: | Raeesa Ganey <Raeesa.ganey at wits.ac.za> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | prinsurf results |
Reference manual: | prinsurf.pdf |
Vignettes: |
Principal Surfaces (source, R code) |
Package source: | prinsurf_1.0.tar.gz |
Windows binaries: | r-devel: prinsurf_1.0.zip, r-release: prinsurf_1.0.zip, r-oldrel: prinsurf_1.0.zip |
macOS binaries: | r-devel (arm64): prinsurf_1.0.tgz, r-release (arm64): prinsurf_1.0.tgz, r-oldrel (arm64): prinsurf_1.0.tgz, r-devel (x86_64): prinsurf_1.0.tgz, r-release (x86_64): prinsurf_1.0.tgz, r-oldrel (x86_64): prinsurf_1.0.tgz |
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