Generation of tuning constant for Mahalanobis fixed point clusters.
Description
Generates tuning constants ca
for fixmahal
dependent on
the number of points and variables of the current fixed point cluster
(FPC).
This is experimental and only thought for use in fixmahal
.
Usage
1 
Arguments
n 
positive integer. Number of points. 
p 
positive integer. Number of variables. 
nmin 
integer larger than 1. Smallest number of points for which

cmin 
positive number. Minimum value for 
nc1 
positive integer. Number of points at which 
c1 
positive numeric. Tuning constant for 
q 
numeric between 0 and 1. 1 for steepest possible descent of

Details
Some experiments suggest that the tuning constant ca
should
decrease with increasing FPC size and increase with increasing
p
in fixmahal
. This is to prevent too small
meaningless FPCs while maintaining the significant larger
ones. cmahal
with q=1
computes ca
in such a way
that as long as ca>cmin
, the decrease in n
is as steep
as possible in order to maintain the validity of the convergence
theorem in Hennig and Christlieb (2002).
Value
A numeric vector of length n
, giving the values for ca
for all FPC sizes smaller or equal to n
.
Author(s)
Christian Hennig c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
References
Hennig, C. and Christlieb, N. (2002) Validating visual clusters in large datasets: Fixed point clusters of spectral features, Computational Statistics and Data Analysis 40, 723739.
See Also
fixmahal
Examples
1 2 