R: Weighted Duembgen's Shape Matrix

duembgen.shape.wt {ICSNP} | R Documentation

## Weighted Duembgen's Shape Matrix

### Description

Iterative algorithm to estimate the weighted version of Dümbgen's shape matrix.

### Usage

    duembgen.shape.wt(X, wt = rep(1, nrow(X)), init = NULL,
                      eps = 1e-06, maxiter = 100, na.action = na.fail)

### Arguments

`X` | numeric data frame or matrix.
`wt` | vector of weights. Should be nonnegative and at least one larger than zero.
`init` | an optional matrix giving the starting value for the iteration.
`eps` | convergence tolerance.
`maxiter` | maximum number of iterations.
`na.action` | a function which indicates what should happen when the data contain 'NA's. Default is to fail.

### Details

The weighted Dümbgen shape matrix can be seen as `tyler.shape`'s matrix wrt to the origin for the weighted pairwise differences of the observations. Therefore this shape matrix needs no location parameter.

Note that this function is memory comsuming and slow for large data sets since the matrix is based on all pairwise difference of the observations.

### Value

a matrix.

### Author(s)

Klaus Nordhausen, klaus.nordhausen@uta.fi

### References

Sirkiä, S., Taskinen, S. and Oja, H. (2007), Symmetrised M-estimators of scatter. Journal of Multivariate Analysis, 98, 1611–1629.

### See Also

`duembgen.shape`

### Examples

    set.seed(1)
    cov.matrix.1 <- matrix(c(3,2,1,2,4,-0.5,1,-0.5,2), ncol = 3)
    X.1 <- rmvnorm(100, c(0,0,0), cov.matrix.1)
    cov.matrix.2 <- diag(1,3)
    X.2 <- rmvnorm(50, c(1,1,1), cov.matrix.2)
    X <- rbind(X.1, X.2)

    D1 <-  duembgen.shape.wt(X, rep(c(0,1), c(100,50)))
    D2 <-  duembgen.shape.wt(X, rep(c(1,0), c(100,50)))

    D1
    D2

    rm(.Random.seed)

[Package ICSNP version 1.1-0 Index]