| predict.ellipsoid {cluster} | R Documentation |
Compute points on the ellipsoid boundary, mostly for drawing.
predict.ellipsoid(object, n.out=201, ...) ## S3 method for class 'ellipsoid': predict(object, n.out=201, ...) ellipsoidPoints(A, d2, loc, n=201)
object |
an object of class ellipsoid, typically from
ellipsoidhull(); alternatively any list-like object
with proper components, see details below. |
n.out,n |
the number of points to create. |
A, d2, loc |
arguments of the auxilary ellipsoidPoints,
see below. |
... |
passed to and from methods. |
Note ellipsoidPoints is the workhorse function of
predict.ellipsoid a standalone function and method for
ellipsoid objects, see ellipsoidhull.
The class of object is not checked; it must solely have valid
components loc (length p), the p x p
matrix cov (corresponding to A) and d2 for the
center, the shape (``covariance'') matrix and the squared average
radius or distance.
a numeric matrix of dimension n.out times p.
## see also example(ellipsoidhull)
## Robust vs. L.S. covariance matrix
set.seed(143)
x <- rt(200, df=3)
y <- 3*x + rt(200, df=2)
plot(x,y, main="non-normal data (N=200)")
X <- cbind(x,y)
C.ls <- cov(X) ; m.ls <- colMeans(X)
lines(ellipsoidPoints(C.ls, d2 = 2, loc=m.ls), col="green")
if(require(MASS)) {
Cxy <- cov.rob(cbind(x,y))
lines(ellipsoidPoints(Cxy$cov, d2 = 2, loc=Cxy$center), col="red")
}# MASS