pbadepth:

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

Usage

pbadepth(x, est = onestep, con = 0, alpha = 0.05, nboot = 2000, grp = NA, op = 1, allp = TRUE, MM = FALSE, MC = FALSE, cop = 3, SEED = TRUE, na.rm = FALSE, ...)

Arguments

x
est
con
alpha
nboot
grp
op
allp
MM
MC
cop
SEED
na.rm
...

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, est = onestep, con = 0, alpha = 0.05, nboot = 2000, 
    grp = NA, op = 1, allp = TRUE, MM = FALSE, MC = FALSE, cop = 3, 
    SEED = TRUE, na.rm = FALSE, ...) 
{
    con <- as.matrix(con)
    if (is.matrix(x) || is.data.frame(x)) 
        x = listm(x)
    if (!is.list(x)) 
        stop("Data must be stored in list mode or in matrix mode.")
    if (!is.na(grp)) {
        xx <- list()
        for (i in 1:length(grp)) xx[[i]] <- x[[grp[i]]]
        x <- xx
    }
    J <- length(x)
    mvec <- NA
    nvec = NA
    for (j in 1:J) {
        temp <- x[[j]]
        if (na.rm) 
            temp <- temp[!is.na(temp)]
        x[[j]] <- temp
        mvec[j] <- est(temp, ...)
        nvec[j] = length(temp)
    }
    Jm <- J - 1
    d <- ifelse(con == 0, (J^2 - J)/2, ncol(con))
    if (sum(con^2) == 0) {
        if (allp) {
            con <- matrix(0, J, d)
            id <- 0
            for (j in 1:Jm) {
                jp <- j + 1
                for (k in jp:J) {
                  id <- id + 1
                  con[j, id] <- 1
                  con[k, id] <- 0 - 1
                }
            }
        }
        if (!allp) {
            con <- matrix(0, J, Jm)
            for (j in 1:Jm) {
                jp <- j + 1
                con[j, j] <- 1
                con[jp, j] <- 0 - 1
            }
        }
    }
    bvec <- matrix(NA, nrow = J, ncol = nboot)
    if (SEED) 
        set.seed(2)
    for (j in 1:J) {
        data <- matrix(sample(x[[j]], size = length(x[[j]]) * 
            nboot, replace = TRUE), nrow = nboot)
        bvec[j, ] <- apply(data, 1, est, na.rm = na.rm, ...)
    }
    chkna = sum(is.na(bvec))
    if (chkna > 0) {
        print("Bootstrap estimates of location could not be computed")
        print("This can occur when using an M-estimator")
        print("Might try est=tmean")
    }
    bcon <- t(con) %*% bvec
    tvec <- t(con) %*% mvec
    tvec <- tvec[, 1]
    tempcen <- apply(bcon, 1, mean)
    vecz <- rep(0, ncol(con))
    bcon <- t(bcon)
    smat <- var(bcon - tempcen + tvec)
    temp <- bcon - tempcen + tvec
    bcon <- rbind(bcon, vecz)
    if (op == 1) 
        dv <- mahalanobis(bcon, tvec, smat)
    if (op == 2) {
        smat <- cov.mcd(temp)$cov
        dv <- mahalanobis(bcon, tvec, smat)
    }
    if (op == 3) {
        if (!MC) 
            dv <- pdis(bcon, MM = MM, cop = cop)
        if (MC) 
            dv <- pdisMC(bcon, MM = MM, cop = cop)
    }
    bplus <- nboot + 1
    sig.level <- 1 - sum(dv[bplus] >= dv[1:nboot])/nboot
    list(p.value = sig.level, psihat = tvec, con = con, n = nvec)
  }

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