rrcov: covMest-deprecated – R documentation

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covMest-deprecated

Constrained M-Estimates of Location and Scatter

Description

Computes constrained M-Estimates of multivariate location and scatter based on the translated biweight function (‘t-biweight’) using a High breakdown point initial estimate. The default initial estimate is the Minimum Volume Ellipsoid computed with CovMve. The raw (not reweighted) estimates are taken and the covariance matrix is standardized to determinant 1.

Usage

covMest(x, cor=FALSE, r = 0.45, arp = 0.05, eps=1e-3,
    maxiter=120, control, t0, S0)

Arguments

`x`	a matrix or data frame.
`cor`	should the returned result include a correlation matrix? Default is `cor = FALSE`

`r`	required breakdown point. Allowed values are between `(n - p)/(2 * n)` and 1 and the default is 0.45
`arp`	asympthotic rejection point, i.e. the fraction of points receiving zero weight (see Rocke (1996)). Default is `0.05`.
`eps`	a numeric value specifying the relative precision of the solution of the M-estimate. Defaults to `1e-3`
`maxiter`	maximum number of iterations allowed in the computation of the M-estimate. Defaults to 120
`control`	a list with estimation options - same as these provided in the fucntion specification. If the control object is supplied, the parameters from it will be used. If parameters are passed also in the invocation statement, they will override the corresponding elements of the control object.
`t0`	optional initial high breakdown point estimates of the location. If not supplied MVE will be used.
`S0`	optional initial high breakdown point estimates of the scatter. If not supplied MVE will be used.

Details

Rocke (1996) has shown that the S-estimates of multivariate location and scatter in high dimensions can be sensitive to outliers even if the breakdown point is set to be near 0.5. To mitigate this problem he proposed to utilize the translated biweight (or t-biweight) method with a standardization step consisting of equating the median of rho(d) with the median under normality. This is then not an S-estimate, but is instead a constrained M-estimate. In order to make the smooth estimators to work, a reasonable starting point is necessary, which will lead reliably to a good solution of the estimator. In covMest the MVE computed by CovMve is used, but the user has the possibility to give her own initial estimates.

Value

An object of class "mest" which is basically a list with the following components. This class is "derived" from "mcd" so that the same generic functions - print, plot, summary - can be used. NOTE: this is going to change - in one of the next revisions covMest will return an S4 class "mest" which is derived (i.e. contains) form class "cov".

`center`	the final estimate of location.
`cov`	the final estimate of scatter.
`cor`	the estimate of the correlation matrix (only if `cor = TRUE`).
`mah`	mahalanobis distances of the observations using the M-estimate of the location and scatter.
`X`	the input data as a matrix.
`n.obs`	total number of observations.
`method`	character string naming the method (M-Estimates).
`call`	the call used (see `match.call`).

Note

The psi, rho and weight functions for the M estimation are encapsulated in a virtual S4 class PsiFun from which a PsiBwt class, implementing the translated biweight (t-biweight), is dervied. The base class PsiFun contains also the M-iteration itself. Although not documented and not accessibale directly by the user these classes will form the bases for adding other functions (biweight, LWS, etc.) as well as S-estimates.

Author(s)

Valentin Todorov valentin.todorov@chello.at,

(some code from C. Becker - http://www.sfb475.uni-dortmund.de/dienst/de/content/struk-d/bereicha-d/tpa1softw-d.html)

References

D.L.Woodruff and D.M.Rocke (1994) Computable robust estimation of multivariate location and shape on high dimension using compound estimators, Journal of the American Statistical Association, 89, 888–896.

D.M.Rocke (1996) Robustness properties of S-estimates of multivariate location and shape in high dimension, Annals of Statistics, 24, 1327-1345.

D.M.Rocke and D.L.Woodruff (1996) Identification of outliers in multivariate data Journal of the American Statistical Association, 91, 1047–1061.

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.