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.

Copy Link

Version

Install

install.packages('LPCM')

Monthly Downloads

668

Version

0.47-6

License

GPL (>= 2)

Maintainer

Jochen Einbeck

Last Published

August 30th, 2024

Functions in LPCM (0.47-6)

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

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.