rmanovab1:

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

Usage

rmanovab1(x, tr = 0.2, grp = c(1:length(x)))

Arguments

x
tr
grp

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, tr = 0.2, grp = c(1:length(x))) 
{
    if (!is.list(x) && !is.matrix(x)) 
        stop("Data must be stored in a matrix or in list mode.")
    if (is.list(x)) {
        J <- length(grp)
        m1 <- matrix(x[[grp[1]]], length(x[[grp[1]]]), 1)
        for (i in 2:J) {
            m2 <- matrix(x[[grp[i]]], length(x[[i]]), 1)
            m1 <- cbind(m1, m2)
        }
    }
    if (is.matrix(x)) {
        if (length(grp) < ncol(x)) 
            m1 <- as.matrix(x[, grp])
        if (length(grp) >= ncol(x)) 
            m1 <- as.matrix(x)
        J <- ncol(x)
    }
    m2 <- matrix(0, nrow(m1), ncol(m1))
    xvec <- 1
    g <- floor(tr * nrow(m1))
    for (j in 1:ncol(m1)) {
        m2[, j] <- winval(m1[, j], tr)
        xvec[j] <- mean(m1[, j], tr)
    }
    xbar <- mean(xvec)
    qc <- (nrow(m1) - 2 * g) * sum((xvec - xbar)^2)
    m3 <- matrix(0, nrow(m1), ncol(m1))
    m3 <- sweep(m2, 1, apply(m2, 1, mean))
    m3 <- sweep(m3, 2, apply(m2, 2, mean))
    m3 <- m3 + mean(m2)
    qe <- sum(m3^2)
    test <- (qc/(qe/(nrow(m1) - 2 * g - 1)))
    v <- winall(m1)$cov
    vbar <- mean(v)
    vbard <- mean(diag(v))
    vbarj <- 1
    for (j in 1:J) {
        vbarj[j] <- mean(v[j, ])
    }
    A <- J * J * (vbard - vbar)^2/(J - 1)
    B <- sum(v * v) - 2 * J * sum(vbarj^2) + J * J * vbar^2
    ehat <- A/B
    etil <- (nrow(m2) * (J - 1) * ehat - 2)/((J - 1) * (nrow(m2) - 
        1 - (J - 1) * ehat))
    etil <- min(1, etil)
    df1 <- (J - 1) * etil
    df2 <- (J - 1) * etil * (nrow(m2) - 2 * g - 1)
    siglevel <- 1 - pf(test, df1, df2)
    list(test = test, df = c(df1, df2), p.value = siglevel, tmeans = xvec, 
        ehat = ehat, etil = etil)
  }

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