LPCM (version 0.43)

lpc.control: Auxiliary parameters for controlling local principal curves.

Description

This function bundles parameters controlling mainly the convergence-, boundary-, and stopping-behaviour of the local principal curve. It will be used only inside the lpc() function argument.

Usage

lpc.control(iter =100, boundary = 0.005, convergence.at =0.00001,
     pruning.thresh=0.0, rho0=0.4, cross=TRUE)

Arguments

iter

Maximum number of iterations on either side of the starting point within each branch.

boundary

This boundary correction [2] reduces the bandwidth adaptively once the relative difference of parameter values between two centers of mass falls below the given threshold. This measure delays convergence and enables the curve to proceed further into the e

convergence.at

This forces the curve to stop if the relative difference of parameter values between two centers of mass falls below the given threshold. If set to 0, then the curve will always stop after exactly iter iterations.

pruning.thresh

Prunes branches corresponding to higher-depth starting points if their density estimate falls below this threshold. Typically, a value between 0.0 and 1.0. The setting 0.0 means no pruning.

rho0

A numerical value which steers the birth process of higher-depth starting points. Usually, between 0.3 and 0.4 (see reference [1]).

cross

Logical parameter. If TRUE, curves are stopped when they come too close to an existing branch. Used in the self-coverage function.

Value

• A list of the six specified parameters, which can be read by the control argument of the lpc function.

References

[1] Einbeck, J., Tutz, G. & Evers, L. (2005): Exploring Multivariate Data Structures with Local Principal Curves. In: Weihs, C. and Gaul, W. (Eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg, pages 256-263.

[2] Einbeck, J. and Zayed, M. (2011). Some asymptotics for localized principal components and curves. Working paper, Durham University. Unpublished.

Examples

data(calspeedflow)
fit1 <- lpc(calspeedflow[,c(3,4)], x0=c(50,60),scaled=TRUE,
   control=lpc.control(iter=20, boundary=0))
plot(fit1, type=c("curve","start","mass"))