rrcov.control {rrcov} | R Documentation |
Useful for passing the estimation options as parameters to the estimation functions
rrcov.control(alpha=1/2, nsamp=500, seed=NULL, tolSolve=10e-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)