FRB (version 2.0-1)

MMest_loccov: S- and MM-Estimates of multivariate location and covariance matrix

Description

Compute S- and MM-Estimates of multivariate location and covariance matrix

Usage

MMest_loccov(Y, control=MMcontrol(...), ...)
    Sest_loccov(Y, bdp=.5, control=Scontrol(...), ...)
    MMest_twosample(X, groups, control=MMcontrol(...), ...)
    Sest_twosample(X, groups, bdp=0.5, control=Scontrol(...), ...)

Value

Return lists with the following components:

Mu

location

Gamma

shape

scale

scale=det^(1/(2*m))

Sigma

covariance matrix

c1

tuning parameter of the loss function for MM-estimation

SMu

location of the initial S-estimate

SGamma

shape of the initial S-estimate

SSigma

covariance matrix of the initial S-estimate

b

tuning parameters used in Tukey biweight loss function for S-estimation, as determined by bdp

w

scaled weights

outflag

outlier flags

Arguments

Y

input matrix or data frame

X

input matrix or data frame

bdp

breakdown point, defaults to 0.5

groups

grouping variable

control

a list with control parameters for tuning the S- or MM-estimate and its computing algorithm, seeScontrol and MMcontrol.

...

further arguments to be passed to CovMMest()

Details

This functions are internal, wrappers around the functions Sest() CovMMest().

Examples

# \donttest{
    Y <- matrix(rnorm(50*5), ncol=5)
    (MMests <- MMest_loccov(Y)) 

    (Sests <- Sest_loccov(Y, bdp = 0.25)) 

    Y1 <- matrix(rnorm(50*5), ncol=5)
    Y2 <- matrix(rnorm(50*5), ncol=5)
    Ybig <- rbind(Y1,Y2)
    grp <- c(rep(1,50),rep(2,50))
    (MMests <- MMest_twosample(Ybig, grp))
# }