ellipsoidhull: Compute the Ellipsoid Hull or Spanning Ellipsoid of a Point...

In cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.

Compute the Ellipsoid Hull or Spanning Ellipsoid of a Point Set

Description

Compute the “ellipsoid hull” or “spanning ellipsoid”, i.e. the ellipsoid of minimal volume (‘area’ in 2D) such that all given points lie just inside or on the boundary of the ellipsoid.

Usage

ellipsoidhull(x, tol=0.01, maxit=5000,
              ret.wt = FALSE, ret.sqdist = FALSE, ret.pr = FALSE)
## S3 method for class 'ellipsoid'
print(x, digits = max(1, getOption("digits") - 2), ...)

Arguments

x	the n p-dimensional points asnumeric n\times p matrix.
tol	convergence tolerance for Titterington's algorithm. Setting this to much smaller values may drastically increase the number of iterations needed, and you may want to increas maxit as well.
maxit	integer giving the maximal number of iteration steps for the algorithm.
ret.wt, ret.sqdist, ret.pr	logicals indicating if additional information should be returned, ret.wt specifying the weights, ret.sqdist the squared distances and ret.pr the final probabilities in the algorithms.
digits,...	the usual arguments to print methods.

Details

The “spanning ellipsoid” algorithm is said to stem from Titterington(1976), in Pison et al (1999) who use it for clusplot.default.
The problem can be seen as a special case of the “Min.Vol.” ellipsoid of which a more more flexible and general implementation is cov.mve in the MASS package.

Value

an object of class "ellipsoid", basically a list with several components, comprising at least

cov	p\times p covariance matrix description the ellipsoid.
loc	p-dimensional location of the ellipsoid center.
d2	average squared radius. Further, d2 = t^2, where t is “the value of a t-statistic on the ellipse boundary” (from ellipse in the ellipse package), and hence, more usefully, d2 = qchisq(alpha, df = p), where alpha is the confidence level for p-variate normally distributed data with location and covariance loc and cov to lie inside the ellipsoid.
wt	the vector of weights iff ret.wt was true.
sqdist	the vector of squared distances iff ret.sqdist was true.
prob	the vector of algorithm probabilities iff ret.pr was true.
it	number of iterations used.
tol, maxit	just the input argument, see above.
eps	the achieved tolerance which is the maximal squared radius minus p.
ierr	error code as from the algorithm; 0 means ok.
conv	logical indicating if the converged. This is defined as it < maxit && ierr == 0.

Author(s)

Martin Maechler did the present class implementation; Rousseeuw et al did the underlying original code.

References

Pison, G., Struyf, A. and Rousseeuw, P.J. (1999) Displaying a Clustering with CLUSPLOT, Computational Statistics and Data Analysis, 30, 381–392.

D.M. Titterington (1976) Algorithms for computing D-optimal design on finite design spaces. In Proc.\ of the 1976 Conf.\ on Information Science and Systems, 213–216; John Hopkins University.

See Also

predict.ellipsoid which is also the predict method for ellipsoid objects. volume.ellipsoid for an example of ‘manual’ ellipsoid object construction;
further ellipse from package ellipse and ellipsePoints from package sfsmisc.

chull for the convex hull, clusplot which makes use of this; cov.mve.

Examples

x <- rnorm(100)
xy <- unname(cbind(x, rnorm(100) + 2*x + 10))
exy. <- ellipsoidhull(xy)
exy. # >> calling print.ellipsoid()

plot(xy, main = "ellipsoidhull(<Gauss data>) -- 'spanning points'")
lines(predict(exy.), col="blue")
points(rbind(exy.$loc), col = "red", cex = 3, pch = 13)

exy <- ellipsoidhull(xy, tol = 1e-7, ret.wt = TRUE, ret.sq = TRUE)
str(exy) # had small 'tol', hence many iterations
(ii <- which(zapsmall(exy $ wt) > 1e-6))
## --> only about 4 to 6  "spanning ellipsoid" points
round(exy$wt[ii],3); sum(exy$wt[ii]) # weights summing to 1
points(xy[ii,], pch = 21, cex = 2,
       col="blue", bg = adjustcolor("blue",0.25))

cluster documentation built on Nov. 28, 2023, 1:07 a.m.