R: Auxiliary parameters for controlling local principal curves.
lpc.control
R Documentation
Auxiliary parameters for controlling local principal curves.
Description
This function bundles parameters controlling mainly the starting-, convergence-, boundary-,
and stopping-behaviour of the local principal curve. It will be used
only inside the lpc() function argument.
Maximum number of iterations on either side of the starting point within each branch.
cross
Logical parameter. If FALSE, a curve is stopped when it
comes too close to an another part of itself. Note: Even when
cross=FALSE, different branches of the curve (for higher depth
or multiple starting points) are still allowed
to cross. This option only avoids crossing of each particular branch
with itself. Used in the self-coverage functions to avoid overfitting.
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 end points. If set to 0,
this boundary correction is switched off.
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.
mult
numerical value which enforeces a fixed number of starting points. If the
number given here is larger than the number of starting points
provided at x0, then the missing points will be set at
random (For example, if d=2, mult=3, and
x0=c(58.5, 17.8, 80,20), then one gets the starting points (58.5, 17.8), (80,20), and a randomly
chosen third one. Another example for such a situation is x0=NULL with
mult=1, in which one random starting point is chosen). If the number given here is smaller the number of starting points
provided at x0, then only the first mult starting
points will be used.
ms.h
sets the bandwidth (vector) for the initial mean shift procedure
which finds the local density modes, and, hence, the starting points
for the LPC. If unspecified, the bandwidth h used in
function lpc is used here too.
ms.sub
proportion of data points (default=30) which are used to initialize
mean shift trajectories for the mode finding. In fact, we use
min(max(ms.sub, floor(ms.sub*N/100)), 10*ms.sub)
trajectories.
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]).
Value
A list of the nine specified imput parameters, which can be read by the
control argument of the lpc function.
Author(s)
JE
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.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
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> library(LPCM)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LPCM/lpc.control.Rd_%03d_medium.png", width=480, height=480)
> ### Name: lpc.control
> ### Title: Auxiliary parameters for controlling local principal curves.
> ### Aliases: lpc.control
>
> ### ** 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"))
>
>
>
>
>
> dev.off()
null device
1
>