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LPCM

This package can be used for fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with additional functionalities for mean shift clustering.

library(LPCM)
data(calspeedflow)
lpc1 <- lpc(calspeedflow[,3:4])
plot(lpc1, lwd=2, curvecol="red")


ms1 <- ms(calspeedflow[,3:4], plot=FALSE)
plot(ms1)

Try ?LPCM, ?lpc and ?ms.

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Version

Install

install.packages('LPCM')

Monthly Downloads

1,029

Version

0.47-6

License

GPL (>= 2)

Maintainer

Jochen Einbeck

Last Published

August 30th, 2024

Functions in LPCM (0.47-6)

ms

Mean shift clustering.
lpc.spline.auxiliary.functions

Auxiliary functions for spline fitting and projection.
print.lpc

Printing output for lpc, lpc.spline, and ms objects
plot.lpc

Plotting local principal curves and mean shift trajectories
ms.rep

Mean shift procedures.
lpc.project

Projection onto LPC
unscale

Unscaling local principal objects.
lpc.spline

Representing local principal curves through a cubic spline.
followx

Fit an individual branch of a local principal curve.
calspeedflow

Speed-flow data from California.
lpc.control

Auxiliary parameters for controlling local principal curves.
kernels.and.distances

Auxiliary kernel and distance functions.
gaia

Gaia data
Rc

Measuring goodness-of-fit for principal objects.
gvessel

North Atlantic Water Temperature Data.
LPCM-package

Local principal curve methods
lpc

Local principal curves
coverage

Coverage and self-coverage plots.