bbwmcppb.sub:

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

Usage

bbwmcppb.sub(J, K, L, x, est = tmean, JKL = J * K * L, con = 0, alpha = 0.05, grp = c(1:JKL), nboot = 500, bhop = FALSE, SEED = TRUE, ...)

Arguments

J
K
L
x
est
JKL
con
alpha
grp
nboot
bhop
SEED
...

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 (J, K, L, x, est = tmean, JKL = J * K * L, con = 0, 
    alpha = 0.05, grp = c(1:JKL), nboot = 500, bhop = FALSE, 
    SEED = TRUE, ...) 
{
    if (is.matrix(x)) {
        y <- list()
        for (j in 1:ncol(x)) y[[j]] <- x[, j]
        x = y
    }
    ncon = ncol(con)
    p <- J * K * L
    if (p > length(x)) 
        stop("JKL is less than the Number of groups")
    JK = J * K
    KL = K * L
    data <- list()
    xx = list()
    for (j in 1:length(x)) {
        xx[[j]] = x[[grp[j]]]
    }
    ilow = 1 - L
    iup = 0
    for (j in 1:JK) {
        ilow <- ilow + L
        iup = iup + L
        sel <- c(ilow:iup)
        xx[sel] = listm(elimna(matl(xx[sel])))
    }
    jp <- 1 - L
    kv <- 0
    if (SEED) 
        set.seed(2)
    testA = NA
    bsam = list()
    bdat = list()
    aboot = matrix(NA, nrow = nboot, ncol = ncol(con))
    tvec = NA
    tvec = linhat(x, con, est = est, ...)
    for (ib in 1:nboot) {
        ilow <- 1 - L
        iup = 0
        for (j in 1:JK) {
            ilow <- ilow + L
            iup = iup + L
            nv = length(x[[ilow]])
            bdat[[j]] = sample(nv, size = nv, replace = T)
            for (k in ilow:iup) {
                bsam[[k]] = x[[k]][bdat[[j]]]
            }
        }
        ilow = 0 - L
        iup = 0
        aboot[ib, ] = linhat(bsam, con = con, est = est, ...)
    }
    pbA = NA
    for (j in 1:ncol(aboot)) {
        pbA[j] = mean(aboot[, j] > 0)
        pbA[j] = 2 * min(c(pbA[j], 1 - pbA[j]))
    }
    if (!bhop) 
        dvec = alpha/c(1:ncol(con))
    if (bhop) 
        dvec <- (ncol(con) - c(1:ncol(con)) + 1) * alpha/ncol(con)
    outputA <- matrix(0, ncol(con), 6)
    dimnames(outputA) <- list(NULL, c("con.num", "psihat", "p.value", 
        "p.crit", "ci.lower", "ci.upper"))
    test = pbA
    temp2 <- order(0 - test)
    zvec <- dvec[1:ncon]
    sigvec <- (test[temp2] >= zvec)
    outputA[temp2, 4] <- zvec
    icl <- round(dvec[ncon] * nboot/2) + 1
    icu <- nboot - icl - 1
    outputA[, 2] <- tvec
    for (ic in 1:ncol(con)) {
        outputA[ic, 1] <- ic
        outputA[ic, 3] <- test[ic]
        temp <- sort(aboot[, ic])
        outputA[ic, 5] <- temp[icl]
        outputA[ic, 6] <- temp[icu]
    }
    outputA
  }

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