Description Usage Arguments Details Value Author(s) References See Also Examples
Computation of cluster center points for circular regression data. A cluster method based on redescending M-estimators is used.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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 M-estimators 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 M-estimators with an application to edge identification in images. Journal of Multivariate Analysis, 92(2), 359–385.
bestMclust
, projMclust
,
envMclust
, deldupMclust
1 2 3 4 5 6 7 8 | 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)
|
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