mlrregCI:

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

Usage

mlrregCI(x, y, nboot = 300, MC = FALSE, SEED = TRUE, op.dis = TRUE)

Arguments

x
y
nboot
MC
SEED
op.dis

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, y, nboot = 300, MC = FALSE, SEED = TRUE, op.dis = TRUE) 
{
    if (SEED) 
        set.seed(2)
    if (MC) 
        library(parallel)
    est = mlrreg(x, y)$coef
    pval = est
    n = nrow(x)
    JK = (ncol(x) + 1) * ncol(y)
    vals = matrix(0, nrow = nboot, ncol = JK)
    data <- matrix(sample(n, size = n * nboot, replace = TRUE), 
        nrow = nboot)
    if (!MC) 
        for (ib in 1:nboot) {
            vals[ib, ] = mlrreg(x[data[ib, ], ], y[data[ib, ], 
                ])$coef
        }
    if (MC) {
        data = listm(t(data))
        vals = mclapply(data, mlrreg.est, x, y, mc.preschedule = TRUE)
        vals = t(matl(vals))
    }
    pv = NULL
    for (j in 1:JK) {
        pv[j] = mean(vals[, j] > 0) + 0.5 * mean(vals[, j] == 
            0)
        pv[j] = 2 * min(c(pv[j], 1 - pv[j]))
    }
    ic = 0
    il = 1
    iu = ncol(x) + 1
    for (iy in 1:ncol(y)) {
        pval[, iy] = pv[il:iu]
        il = il + ncol(x) + 1
        iu = iu + ncol(x) + 1
    }
    list(estimates = est, p.values = pval)
  }

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