rrcov.control: Control object for the estimation parameters
In armstrtw/rrcov: Scalable Robust Estimators with High Breakdown Point

Description Usage Arguments Details Value Examples

Useful for passing the estimation options as parameters to the estimation functions

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rrcov.control(alpha=1/2, nsamp=500, seed=NULL, tolSolve=1e-14,
    trace=FALSE, use.correction=TRUE, adjust=FALSE,
    r = 0.45, arp = 0.05, eps=1e-3, maxiter=120)

`alpha`	This parameter controls the size of the subsets over which the determinant is minimized, i.e. `alpha*n` observations are used for computing the determinant. Allowed values are between 0.5 and 1 and the default is 0.5.
`nsamp`	number of subsets used for initial estimates or `"best"` or `"exact"`. Default is `nsamp = 500`. If `nsamp="best"` exhaustive enumeration is done, as far as the number of trials do not exceed 5000. If `nsamp="exact"` exhaustive enumeration will be attempted however many samples are needed. In this case a warning message will be displayed saying that the computation can take a very long time.
`seed`	starting value for random generator. Default is `seed = NULL`
`tolSolve`	numeric tolerance to be used for inversion (`solve`) of the covariance matrix in `mahalanobis`.
`trace`	whether to print intermediate results. Default is `trace = FALSE`
`use.correction`	whether to use finite sample correction factors. Default is `use.correction=TRUE`
`adjust`	whether to perform intercept adjustment at each step. This could be quite time consuming, therefore the default is `adjust = FALSE`
`r`	M-estimates: breakdown point, i.e. the fraction of contaminated data. The default is 0.45
`arp`	M-estimates: asypmthotic rejection point, i.e. the fraction of points receiving zero weights. The default is 0.001
`eps`	M-estimates: the relaive precision of the solution. The default is 1e-3
`maxiter`	M-estimates: maximum number of iterations for the computation of the M-estimates. The default is 120

For details about the estimation options see the corresponding estimation functions.

A list with components, as the parameters passed by the invocation

data(Animals, package = "MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])

ctrl <- rrcov.control(alpha=0.75, trace=TRUE)
covMcd(hbk.x,      control = ctrl)
covMcd(log(brain), control = ctrl)