twomanbt:

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

Usage

twomanbt(x, y, tr = 0.2, alpha = 0.05, nboot = 599)

Arguments

x
y
tr
alpha
nboot

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, tr = 0.2, alpha = 0.05, nboot = 599) 
{
    if (!is.list(x) && !is.matrix(x)) 
        stop("Data must be stored in a matrix or in list mode.")
    if (!is.list(y) && !is.matrix(y)) 
        stop("Data must be stored in a matrix or in list mode.")
    if (is.list(x)) {
        matx <- matrix(0, length(x[[1]]), length(x))
        for (j in 1:length(x)) matx[, j] <- x[[j]]
    }
    if (is.list(y)) {
        maty <- matrix(0, length(y[[1]]), length(y))
        for (j in 1:length(y)) maty[, j] <- y[[j]]
    }
    if (is.matrix(x)) {
        matx <- x
    }
    if (is.matrix(y)) {
        maty <- y
    }
    if (ncol(matx) != ncol(maty)) 
        stop("The number of variables for group one is not equal to the number for group 2")
    if (sum(is.na(mat) >= 1)) 
        stop("Missing values are not allowed.")
    J <- ncol(mat)
    connum <- ncol(matx)
    bvec <- matrix(0, connum, nboot)
    set.seed(2)
    xcen <- matrix(0, nrow(matx), ncol(matx))
    ycen <- matrix(0, nrow(maty), ncol(maty))
    for (j in 1:connum) xcen[, j] <- matx[, j] - mean(matx[, 
        j], tr)
    for (j in 1:connum) ycen[, j] <- maty[, j] - mean(maty[, 
        j], tr)
    print("Taking bootstrap samples. Please wait.")
    bootx <- sample(nrow(matx), size = nrow(matx) * nboot, replace = TRUE)
    booty <- sample(nrow(maty), size = nrow(maty) * nboot, replace = TRUE)
    matval <- matrix(0, nrow = nboot, ncol = connum)
    for (j in 1:connum) {
        datax <- matrix(xcen[bootx, j], ncol = nrow(matx))
        datay <- matrix(ycen[booty, j], ncol = nrow(maty))
        paste("Working on variable", j)
        top <- apply(datax, 1, mean, tr) - apply(datay, 1, mean, 
            tr)
        botx <- apply(datax, 1, trimse, tr)
        boty <- apply(datay, 1, trimse, tr)
        matval[, j] <- abs(top)/sqrt(botx^2 + boty^2)
    }
    bvec <- apply(matval, 1, max)
    icrit <- round((1 - alpha) * nboot)
    bvec <- sort(bvec)
    crit <- bvec[icrit]
    psihat <- matrix(0, ncol = 4, nrow = connum)
    dimnames(psihat) <- list(NULL, c("con.num", "psihat", "ci.lower", 
        "ci.upper"))
    test <- matrix(0, ncol = 3, nrow = connum)
    dimnames(test) <- list(NULL, c("con.num", "test", "se"))
    for (j in 1:ncol(matx)) {
        temp <- yuen(matx[, j], maty[, j], tr = tr)
        test[j, 1] <- j
        test[j, 2] <- abs(temp$test)
        test[j, 3] <- temp$se
        psihat[j, 1] <- j
        psihat[j, 2] <- mean(matx[, j], tr) - mean(maty[, j])
        psihat[j, 3] <- mean(matx[, j], tr) - mean(maty[, j]) - 
            crit * temp$se
        psihat[j, 4] <- mean(matx[, j], tr) - mean(maty[, j]) + 
            crit * temp$se
    }
    list(psihat = psihat, teststat = test, critical.value = crit)
  }

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