Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
This package implements the techniques introduced in Einbeck, Tutz & Evers (2005), and successive related papers.
The main functions to be called by the user are
lpc, for the estimation of the local centers of mass
which make up the principal curve;
lpc.spline, which is a smooth and fully parametrized
cubic spline respresentation of the latter;
lpc.project, which enables to compress data by
projecting them orthogonally onto the curve;
Rc for assessing
lpc.self.coverage for bandwidth selection;
This package also contains some code for density
mode detection (‘local principal points’) and mean shift clustering (as well as bandwidth
selection in this context), which implements the methods presented in
Einbeck (2011). See the help file for
A second R package which will implement the extension of local principal curves to local principal surfaces and manifolds, as proposed in Einbeck, Evers & Powell (2010), is in preparation.
Contributions (in form of pieces of code, or useful suggestions for improvements) by Jo Dwyer, Mohammad Zayed, and Ben Oakley are gratefully acknowledged.
Jochen Einbeck and Ludger Evers
Maintainer: Jochen Einbeck <firstname.lastname@example.org>
Einbeck, J., Tutz, G., & Evers, L. (2005): Local principal curves, Statistics and Computing 15, 301-313.
Einbeck, J., Evers, L., & Powell, B. (2010): Data compression and regression through local principal curves and surfaces, International Journal of Neural Systems, 20, 177-192.
Einbeck, J. (2011): Bandwidth selection for nonparametric unsupervised learning techniques – a unified approach via self-coverage. Journal of Pattern Recognition Research 6, 175-192.
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