circMclust {edci} | R Documentation |
Computation of cluster center points for circular regression data. A cluster method based on redescending M-estimators 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) plot.circMclust(x, datax, datay, ccol="black", clty=1, clwd=3, ...) print.circMclust(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,
if method="const" is chosen. |
nr |
optional positive integer. Number of starting radiuses, if
method="const" is chosen. |
prec |
optional positive integer. Tuning parameter for
distinguishing different clusters, which is passed to
deldupMclust . |
minsx, maxsx, minsy, maxsy, minsr |
optional numbers
determining the domain of starting midpoints and the range of
radiuses, if method="const" is chosen. |
maxsr |
optional number determining the maximum radius used as
starting value. Note, that this is valid for all method s
while minsx , maxsx , minsy , maxsy , and
minsr are only used by method="const" . |
nsc |
optional positive integer. Number of starting circles in each
iteration, if method="prob" is chosen. |
nc |
optional positive integer. Number of clusters to search, if
method="prob" is chosen. Note that, if nc is to
large, e.g. nc clusters cannot be found, the function does not
terminate. Attention! Using Windows, the routine cannot even be
breaked manually in this case! |
minsd, maxsd |
optional positive numbers. Minimal and maximal
distance of starting points which are used, if method="prob"
is chosen. |
brminx, brmaxx, brminy, brmaxy, brminr, brmaxr |
optional
numbers. The maximization is stopped, if the midpoint leaves the
domain [brminx ,brmaxx ]x[brminy ,brmaxy ] or
if the radius leaves [brminr ,brmaxr ]. |
brmaxit |
optional positive integer. Since the maximization could
be very slow in some cases depending on the starting value, the
maximization is stopped after brmaxit iterations. |
x |
object returned by circMclust |
ccol,clty,clwd |
optional graphic parameters used for plotting the circles. |
... |
Additional parameters passed to plot . |
circMclust
implements a cluster method using local
maxima of redescending M-estimators for the case of circular
regression. This method is based on a method introduced by Müller and
Garlipp in 2003 (see references).
See also bestMclust
, projMclust
, and
envMclust
for choosing the 'real' clusters out of the
found.
Return value is a numerical matrix containing one row for every found
cluster circle. The columns "cx" and "cy" are their midpoints and "r"
are the radiuses.
The columns "value" and "count" give the value of the objective
function and the number, how often they are found.
Tim Garlipp, garlipp@mathematik.uni-oldenburg.de
Müller, C.H., Garlipp, T. (2003) Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images, to appear in JMVA.
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)