R: Weighted Symmetrized Huber Scatter Matrix

symm.huber.wt {ICSNP} | R Documentation

## Weighted Symmetrized Huber Scatter Matrix

### Description

Iterative algorithm to estimate the weighted symmetrized Huber scatter matrix.

### Usage

    symm.huber.wt(X, wt = rep(1, nrow(X)), qg = 0.9, 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.
`qg` | tuning parameter. Should be between 0 and 1. The default is 0.9.
`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 symmetrized Huber scatter matrix is the regular Huber scatter matrix for the weighted pairwise differences of the observations taken wrt to the origin.

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

`symm.huber`

### 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 <-  symm.huber.wt(X, rep(c(0,1), c(100,50)))
    D2 <-  symm.huber.wt(X, rep(c(1,0), c(100,50)))

    D1
    D2

    rm(.Random.seed)

[Package ICSNP version 1.1-0 Index]