MM Estimates of Multivariate Location and Scatter
Computes MM-Estimates of multivariate location and scatter starting from an initial S-estimate
CovMMest(x, bdp = 0.5, eff = 0.95, eff.shape=TRUE, maxiter = 50,
trace = FALSE, tolSolve = 1e-7, control)x |
a matrix or data frame. |
bdp |
a numeric value specifying the required
breakdown point. Allowed values are between
0.5 and 1 and the default is |
eff |
a numeric value specifying the required efficiency
for the MM estimates. Default is |
eff.shape |
logical; if TRUE, eff is with regard to shape-efficiency, otherwise location-efficiency. Default is |
maxiter |
maximum number of iterations allowed
in the computation of the S-estimate (bisquare and Rocke type).
Default is |
trace |
whether to print intermediate results. Default is |
tolSolve |
numeric tolerance to be used as a convergence tolerance for the MM-iteration |
control |
a control object (S4) of class |
Computes MM-estimates of multivariate location and scatter starting from an initial S-estimate.
An S4 object of class CovMMest-class which is a subclass of the
virtual class CovRobust-class.
Valentin Todorov valentin.todorov@chello.at
Tatsuoka, K.S. and Tyler, D.E. (2000). The uniqueness of S and M-functionals under non-elliptical distributions. Annals of Statistics 28, 1219–1243
M. Salibian-Barrera, S. Van Aelstt and G. Willems (2006). Principal components analysis based on multivariate MM-estimators with fast and robust bootstrap. Journal of the American Statistical Association 101, 1198–1211.
R. A. Maronna, D. Martin and V. Yohai (2006). Robust Statistics: Theory and Methods. Wiley, New York.
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/.
library(rrcov)
data(hbk)
hbk.x <- data.matrix(hbk[, 1:3])
CovMMest(hbk.x)
## the following four statements are equivalent
c0 <- CovMMest(hbk.x)
c1 <- CovMMest(hbk.x, bdp = 0.25)
c2 <- CovMMest(hbk.x, control = CovControlMMest(bdp = 0.25))
c3 <- CovMMest(hbk.x, control = new("CovControlMMest", bdp = 0.25))
## direct specification overrides control one:
c4 <- CovMMest(hbk.x, bdp = 0.40,
control = CovControlMMest(bdp = 0.25))
c1
summary(c1)
plot(c1)
## Deterministic MM-estmates
CovMMest(hbk.x, control=CovControlMMest(sest=CovControlSest(method="sdet")))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.