Description Usage Arguments Details Value Author(s) References See Also Examples
Computation of center points for regression data by means of orthogonal regression. A cluster method based on redescending Mestimators is used.
1 2 3 4 5 6 7 8 9 10 11 12 13  oregMclust(datax, datay, bw, method = "const",
xrange = range(datax), yrange = range(datay),
prec = 4, na = 1, sa = NULL, nl = 10, nc = NULL,
brmaxit = 1000)
regparm(reg)
## S3 method for class 'oregMclust'
plot(x, datax, datay, prec = 3, rcol = "black",
rlty = 1, rlwd = 3, ...)
## S3 method for class 'oregMclust'
print(x, ...)

datax, datay 
numerical vectors of coordinates of the
observations. Alternatively, a matrix with two or three columns can
be given. Then, the first two columns are interpreted as coordinates of
the observations and, if available, the third is passed to parameter

bw 
positive number. Bandwidth for the cluster method. 
method 
optional string. Method of choosing starting values for maximization. Possible values are:

xrange, yrange 
optional numerical intervals describing the domains of the observations. This is only used for normalization of the data. Note that both intervals should have approximately the same length or should be transformed otherwise. This is not done automatically, since this transformation affects the choice of the bandwidth. 
prec 
optional positive integer. Tuning parameter for
distinguishing different clusters, which is passed to

na 
optional positive integer. Number of angles per observation
used as starting values for 
sa 
optional numerical vector. Angles (within 
nl 
optional positive integer. Number of starting lines in each
iteration for 
nc 
optional positive integer. Number of clusters to search if method

brmaxit 
optional positive integer. Since the maximization could
be very slow in some cases depending on the starting value, the
maximization is stopped after 
reg, x 
object returned from 
rcol, rlty, rlwd 
optional graphic parameters used for plotting regression lines. 
... 
additional parameters passed to 
oregMclust
implements a cluster method based on redescending
Mestimators for the case of orthogonal regression. This method is
introduced by Mueller and Garlipp in 2003 (see references).
regparm
transforms the columns "alpha" and "beta" to
"intersept" and "slope".
See also bestMclust
, projMclust
, and
envMclust
for choosing the 'best' clusters out of all
found clusters.
A numerical matrix containing one row for every found
regression center line. The columns "alpha" and "beta" are their
parameters in the representation (cos(alpha), sin(alpha)) * (x,y)' = beta, where alpha is within [0,2pi)
. For the alternative
representation y = mx + b, the return value can be passed to regparm
.
The columns "value" and "count" give the value of the objective function and the number how often they are found.
Tim Garlipp, [email protected]
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
1 2 3 4 5 6 7 8 9  x = c(rnorm(100, 0, 3), rnorm(100, 5, 3))
y = c(2 * x[1:100]  5, 0.5 * x[101:200] + 30)/2
x = x + rnorm(200, 0, 0.5)
y = y + rnorm(200, 0, 0.5)
reg = oregMclust(x, y, 1, method = "prob")
reg = projMclust(reg, x, y)
reg
plot(bestMclust(reg, 2, crit = "proj"), x, y)

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