lincdm:

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

Usage

lincdm(x, con = 0, alpha = 0.05, q = 0.5, mop = FALSE, nboot = 100, SEED = TRUE)

Arguments

x
con
alpha
q
mop
nboot
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 (x, con = 0, alpha = 0.05, q = 0.5, mop = FALSE, nboot = 100, 
    SEED = TRUE) 
{
    if (mop && SEED) 
        set.seed(2)
    if (is.list(x)) {
        x <- matl(x)
        x <- elimna(x)
    }
    if (is.matrix(x)) 
        x <- listm(x)
    if (!is.list(x)) 
        stop("Data must be stored in a matrix or in list mode.")
    con <- as.matrix(con)
    J <- length(x)
    h <- length(x[[1]])
    w <- vector("numeric", J)
    xbar <- vector("numeric", J)
    for (j in 1:J) {
        if (!mop) 
            xbar[j] <- qest(x[[j]], q = q)
        if (mop) 
            xbar[j] <- median(x[[j]])
    }
    if (sum(con^2) == 0) {
        temp <- qdmcp(x, alpha = alpha, q = q, pr = FALSE)
        test <- temp$test
        psihat <- temp$psihat
        num.sig <- temp$num.sig
    }
    if (sum(con^2) > 0) {
        ncon <- ncol(con)
        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 (alpha != 0.05 && alpha != 0.01) 
            dvec <- alpha/c(1:ncon)
        if (nrow(con) != length(x)) {
            stop("The number of groups does not match the number of contrast coefficients.")
        }
        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", 
            "crit.p.value", "se"))
        df <- length(x[[1]]) - 1
        if (!mop) 
            w <- covmmed(x, q = q)
        if (mop) 
            w <- bootcov(x, nboot = nboot, pr = FALSE)
        for (d in 1:ncol(con)) {
            psihat[d, 1] <- d
            psihat[d, 2] <- sum(con[, d] * xbar)
            cvec <- as.matrix(con[, d])
            sejk <- sqrt(t(cvec) %*% w %*% cvec)
            test[d, 1] <- d
            test[d, 2] <- sum(con[, d] * xbar)/sejk
            test[d, 3] <- 2 * (1 - pt(abs(test[d, 2]), df))
            test[d, 5] <- sejk
        }
        temp1 <- test[, 3]
        temp2 <- order(0 - temp1)
        zvec <- dvec[1:ncon]
        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]
        num.sig <- sum(test[, 3] <= test[, 4])
    }
    list(test = test, psihat = psihat, num.sig = num.sig)
  }

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