circMclust {edci}  R Documentation 
Computation of cluster center points for circular regression data. A cluster method based on redescending Mestimators is used.
circMclust(datax, datay, bw, method = "const", prec = 4, minsx = min(datax), maxsx = max(datax), nx = 10, minsy = min(datay), maxsy = max(datay), ny = 10, minsr = 0.01 * max(datax, datay), maxsr = (max(datax, datay)  min(datax, datay)), nr = 10, nsc = 5, nc = NULL, minsd = NULL, maxsd = NULL, brminx = minsx, brmaxx = maxsx, brminy = minsy, brmaxy = maxsy, brminr = minsr, brmaxr = maxsr, brmaxit = 1000) ## S3 method for class 'circMclust' plot(x, datax, datay, ccol="black", clty=1, clwd=3, ...) ## S3 method for class 'circMclust' print(x, ...)
datax, datay 
numerical vectors of coordinates of the observations. 
bw 
positive number. Bandwidth for the cluster method. 
method 
optional string. Method of choosing starting values for maximization. Possible values are:

nx, ny 
optional positive integer. Number of starting midpoints
for method 
nr 
optional positive integer. Number of starting radiuses
for method 
prec 
optional positive integer. Tuning parameter for
distinguishing different clusters, which is passed to

minsx, maxsx, minsy, maxsy, minsr 
optional numbers
determining the domain of starting midpoints and the range of
radii for method 
maxsr 
optional number determining the maximum radius used as
starting value. Note that this is valid for all methods
while 
nsc 
optional positive integer. Number of starting circles in each
iteration for method 
nc 
optional positive integer. Number of clusters to search if method

minsd, maxsd 
optional positive numbers. Minimal and maximal
distance of starting points which are used for method 
brminx, brmaxx, brminy, brmaxy, brminr, brmaxr 
optional
numbers. The maximization is stopped if the midpoint leaves the
domain [ 
brmaxit 
optional positive integer. Since the maximization could
be very slow in some cases, depending on the starting value, the
maximization is stopped after 
x 
object returned by 
ccol, clty, clwd 
optional graphic parameters used for plotting the circles. 
... 
additional parameters passed to 
circMclust
implements a cluster method using local
maxima of redescending Mestimators for the case of circular
regression. This method is based on a method introduced by Mueller and
Garlipp in 2003 (see references).
See also bestMclust
, projMclust
, and
envMclust
for choosing the 'best' clusters out of all
found clusters.
Numerical matrix containing one row for every found cluster circle. The columns "cx" and "cy" are their midpoints and "r" are the radii.
The columns "value" and "count" give the value of the objective function and the number how often each cluster is found.
Tim Garlipp, TimGarlipp@gmx.de
Mueller, C. H., & Garlipp, T. (2005). Simple consistent cluster methods based on redescending Mestimators with an application to edge identification in images. Journal of Multivariate Analysis, 92(2), 359–385.
bestMclust
, projMclust
,
envMclust
, deldupMclust
z = (1:100 * pi)/50 x = c(sin(z) * 10 + 20, sin(z) * 30 + 80) + rnorm(200,0,2) y = c(cos(z) * 10 + 20, cos(z) * 30 + 80) + rnorm(200,0,2) circ = circMclust(x, y, 5, method = "prob", prec = 1, nsc = 20, minsd = 10, maxsd = 40) bestMclust(circ, 2) plot(bestMclust(circ, 2), x, y)