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

Usage Arguments Examples

1	ancmg(x, y, pool = TRUE, jcen = 1, fr = 1, depfun = fdepth, nmin = 8, op = 3, tr = 0.2, pts = NULL, SEED = TRUE, pr = TRUE, cop = 3, con = 0, nboot = NA, alpha = 0.05, bhop = FALSE)

`x`
`y`
`pool`
`jcen`
`fr`
`depfun`
`nmin`
`op`
`tr`
`pts`
`SEED`
`pr`
`cop`
`con`
`nboot`
`alpha`
`bhop`

##---- 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, pool = TRUE, jcen = 1, fr = 1, depfun = fdepth, 
    nmin = 8, op = 3, tr = 0.2, pts = NULL, SEED = TRUE, pr = TRUE, 
    cop = 3, con = 0, nboot = NA, alpha = 0.05, bhop = FALSE) 
{
    library(MASS)
    output <- NULL
    if (SEED) 
        set.seed(2)
    if (pr) {
        if (op == 1) 
            print("Trimmed means are to be compared. For medians, use op=2")
        if (op == 2) 
            print("Medians are to be compared. For trimmed means, use op=1")
        if (op == 3) 
            print("trimmed means are compared. For medians, use op=4")
        if (op == 4) 
            print("medians are compared. For trimmed means, use op=3")
    }
    nval <- NA
    if (is.matrix(y)) 
        J <- ncol(y)
    if (is.list(y)) 
        J <- length(y)
    if (is.list(x)) 
        pval <- ncol(x[[1]])
    if (J == 1) 
        stop("Only have one group stored in y")
    if (pval == 1) 
        stop("For one covariate only, using ancgm1")
    if (is.matrix(x)) {
        if (ncol(x)%%J != 0) 
            stop("Number of columns of x should be a multiple of ncol(y)")
    }
    if (is.matrix(x)) {
        pval <- ncol(x)/J
        if (pval == 1) 
            stop("For one covariate only, using ancgm1")
        temp <- seq(1, ncol(x), pval)
        js <- temp[jcen]
        jcenp <- js + pval - 1
        if (jcenp > ncol(x)) 
            stop("jcen has an invalid value")
        xcen <- x[, js:jcenp]
    }
    if (is.list(x)) 
        xcen <- x[[jcen]]
    if (pool) {
        if (is.matrix(x)) 
            xval <- stackit(x, pval)
        if (is.list(x)) 
            xval <- stacklist(x)
        mval <- cov.mve(xval)
        if (is.null(pts)) 
            pts <- ancdes(xval, depfun = depfun, cop = cop)
    }
    if (!pool) {
        if (is.null(pts)) 
            pts <- ancdes(xcen, depfun = depfun, cop = cop)
        mval <- cov.mve(xcen)
    }
    npts = 1
    if (is.matrix(pts)) 
        npts = nrow(pts)
    nval <- matrix(NA, ncol = J, nrow = npts)
    icl <- 0 - pval + 1
    icu <- 0
    for (j in 1:J) {
        icl <- icl + pval
        icu <- icu + pval
        for (i in 1:nrow(pts)) {
            if (is.matrix(x) && is.matrix(y)) {
                nval[i, j] <- length(y[near3d(x[, icl:icu], pts[i, 
                  ], fr, mval), j])
            }
            if (is.matrix(x) && is.list(y)) {
                tempy <- y[[j]]
                nval[i, j] <- length(tempy[near3d(x[, icl:icu], 
                  pts[i, ], fr, mval)])
            }
            if (is.list(x) && is.matrix(y)) {
                xm <- as.matrix(x[[j]])
                nval[i, j] <- length(y[near3d(xm, pts[i, ], fr, 
                  mval), j])
            }
            if (is.list(x) && is.list(y)) {
                tempy <- y[[j]]
                xm <- as.matrix(x[[j]])
                nval[i, j] <- length(tempy[near3d(xm, pts[i, 
                  ], fr, mval)])
            }
        }
    }
    flag <- rep(T, nrow(pts))
    for (i in 1:npts) {
        if (min(nval[i, ]) < nmin) 
            flag[i] <- F
    }
    nflag <- F
    if (sum(flag) == 0) {
        print("Warning: No design points found with large enough sample size")
        nflag <- T
    }
    flag = as.logical(flag)
    if (!nflag) {
        pts <- pts[flag, ]
        nval <- nval[flag, ]
        if (!is.matrix(pts)) 
            pts <- t(as.matrix(pts))
        output <- matrix(NA, nrow = nrow(pts), ncol = 3)
        dimnames(output) <- list(NULL, c("point", "test.stat", 
            "p-value"))
        if (op == 3 || op == 4) 
            output <- list()
    }
    for (i in 1:nrow(pts)) {
        if (op == 1 || op == 2) 
            output[i, 1] <- i
        icl <- 0 - pval + 1
        icu <- 0
        yval <- list()
        for (j in 1:J) {
            icl <- icl + pval
            icu <- icu + pval
            if (is.matrix(x) && is.matrix(y)) {
                yval[[j]] <- y[near3d(x[, icl:icu], pts[i, ], 
                  fr, mval), j]
            }
            if (is.matrix(x) && is.list(y)) {
                tempy <- y[[j]]
                yval[[j]] <- tempy[near3d(x[, icl:icu], pts[i, 
                  ], fr, mval)]
            }
            if (is.list(x) && is.matrix(y)) {
                yval[[j]] <- y[near3d(x[[j]], pts[i, ], fr, mval), 
                  j]
            }
            if (is.list(x) && is.list(y)) {
                tempy <- y[[j]]
                yval[[j]] <- tempy[near3d(x[[j]], pts[i, ], fr, 
                  mval)]
            }
        }
        if (op == 1) 
            temp <- t1way(yval, tr = tr)
        if (op == 2) {
            print("WARNING: NOT RECOMMENDED FOR DISCRETE DATA WITH TIES")
            print("RECOMMENDATION: Set the argument op=4")
            temp <- med1way(yval, SEED = SEED, pr = FALSE)
        }
        if (op == 1 || op == 2) {
            conout = NULL
            output[i, 3] <- temp$p.value
            output[i, 2] <- temp$TEST
        }
        if (op == 3) {
            output[[i]] = linconpb(yval, alpha = alpha, SEED = SEED, 
                con = con, bhop = bhop, tr = tr, nboot = nboot)
        }
        if (op == 4) {
            output[[i]] <- medpb(yval, alpha = alpha, SEED = SEED, 
                con = con, bhop = bhop, nboot = nboot)
        }
    }
    if (nflag) 
        output <- NULL
    if (op == 1 || op == 2) 
        tempout = output
    if (op == 3 || op == 4) {
        tempout = list()
        conout = list()
        for (j in 1:length(output)) tempout[[j]] = output[[j]]$output
        for (j in 1:length(output)) conout[[j]] = output[[j]]$con
    }
    list(points.chosen = pts, sample.sizes = nval, point = tempout, 
        contrast.coef = conout[[1]])
  }