predict.ellipsoid {cluster} R Documentation

## Predict Method for Ellipsoid Objects

### Description

Compute points on the ellipsoid boundary, mostly for drawing.

### Usage

```predict.ellipsoid(object, n.out=201, ...)
## S3 method for class 'ellipsoid':
predict(object, n.out=201, ...)
ellipsoidPoints(A, d2, loc, n.half = 201)
```

### Arguments

 `object` an object of class `ellipsoid`, typically from `ellipsoidhull()`; alternatively any list-like object with proper components, see details below. `n.out, n.half` half the number of points to create. `A, d2, loc` arguments of the auxilary `ellipsoidPoints`, see below. `...` passed to and from methods.

### Details

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) or `qchisq(*, p)` quantile.

### Value

a numeric matrix of dimension `2*n.out` times p.

`ellipsoidhull`, `volume.ellipsoid`.

### Examples

``` ## 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)")
mtext("with classical and robust cov.matrix ellipsoids")
X <- cbind(x,y)
C.ls <- cov(X) ; m.ls <- colMeans(X)
d2.99 <- qchisq(0.99, df = 2)
lines(ellipsoidPoints(C.ls, d2.99, loc=m.ls), col="green")
if(require(MASS)) {
Cxy <- cov.rob(cbind(x,y))
lines(ellipsoidPoints(Cxy\$cov, d2 = d2.99, loc=Cxy\$center), col="red")
}# MASS
```

[Package cluster version 1.11.5 Index]