These functions compute the ‘coverage coefficient’ R_c for local principal curves, local principal points (i.e., kernel density estimates obtained through iterated mean shift), and other principal objects.
Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
plot.lpc
(Package: LPCM) :
Plotting local principal curves
Takes an object of class lpc or lpc.spline and plots any subset of the following components of the local principal curve: Centers of mass; the curve connecting the local centers of mass; the cubic spline representation of the curve; the projections onto the curve; the starting points.
These functions compute coverages (for any principal object), and self-coverages (only for local principal curves, these may be used for bandwidth selection).
unscale takes an object of type lpc, lpc.spline, or ms, which had been fitted using option scaled=TRUE, and transforms the scaled components back to the original data scale.
Fis a natural cubic spline component-wise through the series of local centers of mass. This provides a continuous parametrization in terms of arc length distance, which can be used to compute a projection index for the original or new data points.
providing 
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