ddmv:

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

Usage

ddmv(n = 100, p = 2, steps = 5, gam = 0.4, outtype = 2, est = 1)

Arguments

n
p
steps
gam
outtype
est

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, outtype = 2, 
    est = 1) 
{
    A <- sqrt(diag(1:p))
    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
    if (outtype == 2) 
        x <- x %*% A
    if (est == 1) {
        covs <- var(x)
        mns <- apply(x, 2, mean)
    }
    if (est == 2) {
        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, ])
    }
    if (est == 3) {
        tem <- apply(x, 2, mad)^2
        covv <- diag(tem)
        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, ])
    }
    MD <- sqrt(mahalanobis(x, mns, covs))
    for (i in 1:steps) {
        md <- sqrt(mahalanobis(x, mns, covs))
        medd <- median(md)
        mns <- apply(x[md <= medd, ], 2, mean)
        covs <- var(x[md <= medd, ])
        rd <- sqrt(mahalanobis(x, mns, covs))
        plot(MD, rd)
        points(MD[1:val], rd[1:val], pch = 15)
        identify(MD, rd)
    }
  }

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