mlrreg:

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

Usage

mlrreg(x, y, cov.fun = cov.mcd, ols.op = TRUE, mcd.op = TRUE, quantile.used = floor(0.75 * n), RES = FALSE, ...)

Arguments

x
y
cov.fun
ols.op
mcd.op
quantile.used
RES
...

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, y, cov.fun = cov.mcd, ols.op = TRUE, mcd.op = TRUE, 
    quantile.used = floor(0.75 * n), RES = FALSE, ...) 
{
    library(MASS)
    if (!is.matrix(y)) 
        stop("y is not a matrix")
    X <- cbind(x, y)
    X <- elimna(X)
    n <- nrow(X)
    qy <- ncol(y)
    qx <- ncol(x)
    qxp1 <- qx + 1
    tqyqx <- qy + qx
    y <- X[, qxp1:tqyqx]
    if (!mcd.op) 
        locscat <- cov.fun(X, ...)
    if (mcd.op) 
        locscat <- cov.mcd(X, quan = quantile.used)
    sig <- locscat$cov
    mu <- locscat$center
    sigxx <- sig[1:qx, 1:qx]
    sigxy <- sig[1:qx, qxp1:tqyqx]
    sigyy <- sig[qxp1:tqyqx, qxp1:tqyqx]
    Bhat <- solve(sigxx) %*% sigxy
    sige <- sigyy - t(Bhat) %*% sigxx %*% Bhat
    sige.inv <- solve(sige)
    Ahat <- t(mu[qxp1:tqyqx] - t(Bhat) %*% mu[1:qx])
    resL <- matrix(nrow = nrow(X), ncol = qy)
    for (i in 1:nrow(X)) resL[i, ] <- y[i, ] - t(Bhat) %*% X[i, 
        1:qx]
    for (j in 1:qy) resL[, j] <- resL[, j] - Ahat[j]
    drL <- NA
    for (i in 1:nrow(X)) drL[i] <- t(resL[i, ]) %*% sige.inv %*% 
        resL[i, ]
    w <- rep(0, nrow(X))
    qdr <- qchisq(0.99, qy)
    iflag <- (drL < qdr)
    w[iflag] <- 1
    term1 <- 0
    vec <- c(1:nrow(X))
    keep <- vec[iflag == 1]
    X <- X[keep, ]
    if (ols.op) 
        output <- lsfit(X[, 1:qx], X[, qxp1:tqyqx])
    if (!ols.op) 
        output <- mopreg(X[, 1:qx], X[, qxp1:tqyqx], KEEP = T)
    yhat = X[, qxp1:tqyqx] - output$residuals
    res = NULL
    if (RES) 
        res = output$residuals
    list(coef = output$coefficients, residuals = res)
  }

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