RobStatTM: KurtSDNew – R documentation

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KurtSDNew

Robust multivariate location and scatter estimators

Description

This function computes robust multivariate location and scatter estimators using both random and deterministic starting points.

Usage

initPP(X, muldirand = 20, muldifix = 10, dirmin = 1000)

Arguments

`X`	a data matrix with observations in rows.
`muldirand`	used to determine the number of random directions (candidates), which is `max(p*muldirand, dirmin)`, where `p` is the number of columns in `X`.
`muldifix`	used to determine the number of random directions (candidates), which is `min(n, 2muldifixp)`.
`dirmin`	minimum number of random directions

Details

This function computes robust multivariate location and scatter using both Pen~a-Prieto and random candidates.

Value

A list with the following components:

`idx`	A zero/one vector with ones in the positions of the suspected outliers
`disma`	Robust squared Mahalanobis distances
`center`	Robust mean estimate
`cova`	Robust covariance matrix estimate
`t`	Outlyingness of data points

Author(s)

Ricardo Maronna, rmaronna@retina.ar, based on original code by D. Pen~a and J. Prieto

References

http://www.wiley.com/go/maronna/robust

Examples

data(bus)
X0 <- as.matrix(bus)
X1 <- X0[,-9]
tmp <- initPP(X1)
round(tmp$cov[1:10, 1:10], 3)
tmp$center

RobStatTM

Robust Statistics: Theory and Methods

v1.0.2

GPL (>= 3)

Authors

Matias Salibian-Barrera [cre], Victor Yohai [aut], Ricardo Maronna [aut], Doug Martin [aut], Gregory Brownson [aut] (ShinyUI), Kjell Konis [aut], Kjell Konis [cph] (erfi), Christophe Croux [ctb] (WBYlogreg, BYlogreg), Gentiane Haesbroeck [ctb] (WBYlogreg, BYlogreg), Martin Maechler [cph] (lmrob.fit, lmrob..M..fit, lmrob.S), Manuel Koller [cph] (lmrob.fit, .vcov.avar1, lmrob.S, lmrob.lar), Matias Salibian-Barrera [aut]

Initial release

2020-03-02