covmba:

In musto101/wilcox_R: What the package does (short line)

Usage

covmba(x, csteps = 5)

Arguments

x
csteps

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, csteps = 5) 
{
    zx <- x
    x <- as.matrix(x)
    p <- dim(x)[2]
    covs <- var(x)
    mns <- apply(x, 2, mean)
    for (i in 1:csteps) {
        md2 <- mahalanobis(x, mns, covs)
        medd2 <- median(md2)
        if (p > 1) {
            mns <- apply(x[md2 <= medd2, ], 2, mean)
            covs <- var(x[md2 <= medd2, ])
        }
        if (p == 1) {
            mns <- mean(x[md2 <= medd2])
            covs <- var(x[md2 <= medd2])
        }
    }
    covb <- covs
    mnb <- mns
    critb <- prod(diag(chol(covb)))
    covv <- diag(p)
    med <- apply(x, 2, median)
    md2 <- mahalanobis(x, center = med, covv)
    medd2 <- median(md2)
    if (p > 1) {
        mns <- apply(x[md2 <= medd2, ], 2, mean)
        covs <- var(x[md2 <= medd2, ])
    }
    if (p == 1) {
        mns <- mean(zx[md2 <= medd2])
        covs <- var(zx[md2 <= medd2])
    }
    for (i in 1:csteps) {
        md2 <- mahalanobis(x, mns, covs)
        medd2 <- median(md2)
        if (p > 1) {
            mns <- apply(x[md2 <= medd2, ], 2, mean)
            covs <- var(x[md2 <= medd2, ])
        }
        if (p == 1) {
            mns <- mean(zx[md2 <= medd2])
            covs <- var(zx[md2 <= medd2])
        }
    }
    crit <- prod(diag(chol(covs)))
    if (crit < critb) {
        critb <- crit
        covb <- covs
        mnb <- mns
    }
    rd2 <- mahalanobis(x, mnb, covb)
    const <- median(rd2)/(qchisq(0.5, p))
    covb <- const * covb
    list(center = mnb, cov = covb)
  }

musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.