rmmcp:

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

Usage

rmmcp(x, con = 0, tr = 0.2, alpha = 0.05, dif = TRUE, hoch = TRUE)

Arguments

x
con
tr
alpha
dif
hoch

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, con = 0, tr = 0.2, alpha = 0.05, dif = TRUE, hoch = TRUE) 
{
    flagcon = F
    if (!is.matrix(x)) 
        x <- matl(x)
    if (!is.matrix(x)) 
        stop("Data must be stored in a matrix or in list mode.")
    con <- as.matrix(con)
    J <- ncol(x)
    xbar <- vector("numeric", J)
    x <- elimna(x)
    nval <- nrow(x)
    h1 <- nrow(x) - 2 * floor(tr * nrow(x))
    df <- h1 - 1
    for (j in 1:J) xbar[j] <- mean(x[, j], tr)
    if (sum(con^2 != 0)) 
        CC <- ncol(con)
    if (sum(con^2) == 0) 
        CC <- (J^2 - J)/2
    ncon <- CC
    if (alpha == 0.05) {
        dvec <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102, 0.00851, 
            0.0073, 0.00639, 0.00568, 0.00511)
        if (ncon > 10) {
            avec <- 0.05/c(11:ncon)
            dvec <- c(dvec, avec)
        }
    }
    if (alpha == 0.01) {
        dvec <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201, 0.00167, 
            0.00143, 0.00126, 0.00112, 0.00101)
        if (ncon > 10) {
            avec <- 0.01/c(11:ncon)
            dvec <- c(dvec, avec)
        }
    }
    if (hoch) 
        dvec <- alpha/c(1:ncon)
    if (alpha != 0.05 && alpha != 0.01) 
        dvec <- alpha/c(1:ncon)
    if (sum(con^2) == 0) {
        flagcon <- T
        psihat <- matrix(0, CC, 5)
        dimnames(psihat) <- list(NULL, c("Group", "Group", "psihat", 
            "ci.lower", "ci.upper"))
        test <- matrix(NA, CC, 6)
        dimnames(test) <- list(NULL, c("Group", "Group", "test", 
            "p.value", "p.crit", "se"))
        temp1 <- 0
        jcom <- 0
        for (j in 1:J) {
            for (k in 1:J) {
                if (j < k) {
                  jcom <- jcom + 1
                  q1 <- (nrow(x) - 1) * winvar(x[, j], tr)
                  q2 <- (nrow(x) - 1) * winvar(x[, k], tr)
                  q3 <- (nrow(x) - 1) * wincor(x[, j], x[, k], 
                    tr)$cov
                  sejk <- sqrt((q1 + q2 - 2 * q3)/(h1 * (h1 - 
                    1)))
                  if (!dif) {
                    test[jcom, 6] <- sejk
                    test[jcom, 3] <- (xbar[j] - xbar[k])/sejk
                    temp1[jcom] <- 2 * (1 - pt(abs(test[jcom, 
                      3]), df))
                    test[jcom, 4] <- temp1[jcom]
                    psihat[jcom, 1] <- j
                    psihat[jcom, 2] <- k
                    test[jcom, 1] <- j
                    test[jcom, 2] <- k
                    psihat[jcom, 3] <- (xbar[j] - xbar[k])
                  }
                  if (dif) {
                    dv <- x[, j] - x[, k]
                    test[jcom, 6] <- trimse(dv, tr)
                    temp <- trimci(dv, alpha = alpha/CC, pr = FALSE, 
                      tr = tr)
                    test[jcom, 3] <- temp$test.stat
                    temp1[jcom] <- temp$p.value
                    test[jcom, 4] <- temp1[jcom]
                    psihat[jcom, 1] <- j
                    psihat[jcom, 2] <- k
                    test[jcom, 1] <- j
                    test[jcom, 2] <- k
                    psihat[jcom, 3] <- mean(dv, tr = tr)
                    psihat[jcom, 4] <- temp$ci[1]
                    psihat[jcom, 5] <- temp$ci[2]
                  }
                }
            }
        }
        temp2 <- order(0 - temp1)
        zvec <- dvec[1:ncon]
        sigvec <- (test[temp2] >= zvec)
        if (sum(sigvec) < ncon) {
            dd <- ncon - sum(sigvec)
            ddd <- sum(sigvec) + 1
            zvec[ddd:ncon] <- dvec[ddd]
        }
        test[temp2, 5] <- zvec
        if (!dif) {
            psihat[, 4] <- psihat[, 3] - qt(1 - alpha/(2 * CC), 
                df) * test[, 6]
            psihat[, 5] <- psihat[, 3] + qt(1 - alpha/(2 * CC), 
                df) * test[, 6]
        }
    }
    if (sum(con^2) > 0) {
        if (nrow(con) != ncol(x)) 
            warning("The number of groups does not match the number\n of contrast coefficients.")
        ncon <- ncol(con)
        psihat <- matrix(0, ncol(con), 4)
        dimnames(psihat) <- list(NULL, c("con.num", "psihat", 
            "ci.lower", "ci.upper"))
        test <- matrix(0, ncol(con), 5)
        dimnames(test) <- list(NULL, c("con.num", "test", "p.value", 
            "p.crit", "se"))
        temp1 <- NA
        for (d in 1:ncol(con)) {
            psihat[d, 1] <- d
            if (!dif) {
                psihat[d, 2] <- sum(con[, d] * xbar)
                sejk <- 0
                for (j in 1:J) {
                  for (k in 1:J) {
                    djk <- (nval - 1) * wincor(x[, j], x[, k], 
                      tr)$cov/(h1 * (h1 - 1))
                    sejk <- sejk + con[j, d] * con[k, d] * djk
                  }
                }
                sejk <- sqrt(sejk)
                test[d, 1] <- d
                test[d, 2] <- sum(con[, d] * xbar)/sejk
                test[d, 5] <- sejk
                temp1[d] <- 2 * (1 - pt(abs(test[d, 2]), df))
            }
            if (dif) {
                for (j in 1:J) {
                  if (j == 1) 
                    dval <- con[j, d] * x[, j]
                  if (j > 1) 
                    dval <- dval + con[j, d] * x[, j]
                }
                temp1[d] <- trimci(dval, tr = tr, pr = FALSE)$p.value
                test[d, 1] <- d
                test[d, 2] <- trimci(dval, tr = tr, pr = FALSE)$test.stat
                test[d, 5] <- trimse(dval, tr = tr)
                psihat[d, 2] <- mean(dval, tr = tr)
            }
        }
        test[, 3] <- temp1
        temp2 <- order(0 - temp1)
        zvec <- dvec[1:ncon]
        sigvec <- (test[temp2, 3] >= zvec)
        if (sum(sigvec) < ncon) {
            dd <- ncon - sum(sigvec)
            ddd <- sum(sigvec) + 1
        }
        test[temp2, 4] <- zvec
        psihat[, 3] <- psihat[, 2] - qt(1 - test[, 4]/2, df) * 
            test[, 5]
        psihat[, 4] <- psihat[, 2] + qt(1 - test[, 4]/2, df) * 
            test[, 5]
    }
    if (flagcon) 
        num.sig <- sum(test[, 4] <= test[, 5])
    if (!flagcon) 
        num.sig <- sum(test[, 3] <= test[, 4])
    list(n = nval, test = test, psihat = psihat, con = con, num.sig = num.sig)
  }

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