R: Predict Method for Ellipsoid Objects

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.

Unfortunately, this is only implemented for p = 2, currently; contributions for p >= 3 are very welcome.

### Value

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

### See Also

`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 2.1.0 Index]