MMest_loccov function

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

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

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(...), ...)

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().

Returns

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

Examples

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))