concsim:

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

Usage

concsim(n = 100, p = 2, steps = 5, gam = 0.4, runs = 20)

Arguments

n
p
steps
gam
runs

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 (n = 100, p = 2, steps = 5, gam = 0.4, runs = 20) 
{
    A <- sqrt(diag(1:p))
    mbact <- 0
    fmcdct <- 0
    mbct <- 0
    madct <- 0
    dgkct <- 0
    for (i in 1:runs) {
        x <- matrix(rnorm(n * p), ncol = p, nrow = n)
        val <- floor(gam * n)
        tem <- 10 + 0 * 1:p
        x[1:val, ] <- x[1:val, ] + tem
        x <- x %*% A
        out <- covmba(x, csteps = steps)
        center <- out$center
        cov <- out$cov
        rd2 <- mahalanobis(x, center, cov)
        if (min(rd2[1:val]) > max(rd2[(val + 1):n])) 
            mbact <- mbact + 1
        covs <- var(x)
        mns <- apply(x, 2, mean)
        for (i in 1:steps) {
            md2 <- mahalanobis(x, mns, covs)
            medd2 <- median(md2)
            mns <- apply(x[md2 <= medd2, ], 2, mean)
            covs <- var(x[md2 <= medd2, ])
        }
        rd2 <- mahalanobis(x, mns, covs)
        if (min(rd2[1:val]) > max(rd2[(val + 1):n])) 
            dgkct <- dgkct + 1
        covv <- diag(p)
        med <- apply(x, 2, median)
        md2 <- mahalanobis(x, center = med, covv)
        medd2 <- median(md2)
        mns <- apply(x[md2 <= medd2, ], 2, mean)
        covs <- var(x[md2 <= medd2, ])
        for (i in 1:steps) {
            md2 <- mahalanobis(x, mns, covs)
            medd2 <- median(md2)
            mns <- apply(x[md2 <= medd2, ], 2, mean)
            covs <- var(x[md2 <= medd2, ])
        }
        rd2 <- mahalanobis(x, mns, covs)
        if (min(rd2[1:val]) > max(rd2[(val + 1):n])) 
            mbct <- mbct + 1
        tem <- apply(x, 2, mad)^2
        covv <- diag(tem)
        md2 <- mahalanobis(x, center = med, covv)
        medd2 <- median(md2)
        mns <- apply(x[md2 <= medd2, ], 2, mean)
        covs <- var(x[md2 <= medd2, ])
        for (i in 1:steps) {
            md2 <- mahalanobis(x, mns, covs)
            medd2 <- median(md2)
            mns <- apply(x[md2 <= medd2, ], 2, mean)
            covs <- var(x[md2 <= medd2, ])
        }
        rd2 <- mahalanobis(x, mns, covs)
        if (min(rd2[1:val]) > max(rd2[(val + 1):n])) 
            madct <- madct + 1
        out <- cov.mcd(x)
        center <- out$center
        cov <- out$cov
        rd2 <- mahalanobis(x, center, cov)
        if (min(rd2[1:val]) > max(rd2[(val + 1):n])) 
            fmcdct <- fmcdct + 1
    }
    list(mbact = mbact, fmcdct = fmcdct, dgkct = dgkct, mbct = mbct, 
        madct = madct)
  }

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