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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 some additional functions 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

795

Version

0.46-7

License

GPL (>= 2)

Maintainer

Jochen Einbeck

Last Published

September 14th, 2020

Functions in LPCM (0.46-7)

Measuring goodness-of-fit for principal objects.

kernels.and.distances

Auxiliary kernel and distance functions.

LPCM-package

Local principal curve methods

gaia

Gaia data

gvessel

North Atlantic Water Temperature Data.

calspeedflow

Speed-flow data from California.

followx

Fit an individual branch of a local principal curve.

lpc.control

Auxiliary parameters for controlling local principal curves.

lpc.project

Projection onto LPC

coverage

Coverage and self-coverage plots.

lpc

Local principal curves

unscale

Unscaling local principal objects.

lpc.spline.auxiliary.functions

Auxiliary functions for spline fitting and projection.

Mean shift clustering.

print.lpc

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

plot.lpc

Plotting local principal curves and mean shift trajectories

lpc.spline

Representing local principal curves through a cubic spline.