gvar2g:

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

Usage

gvar2g(x, y, nboot = 100, DF = TRUE, eop = 1, est = skipcov, alpha = 0.05, cop = 3, op = 1, MM = FALSE, SEED = TRUE, pr = FALSE, fast = FALSE, ...)

Arguments

x
y
nboot
DF
eop
est
alpha
cop
op
MM
SEED
pr
fast
...

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 = 100, DF = TRUE, eop = 1, est = skipcov, 
    alpha = 0.05, cop = 3, op = 1, MM = FALSE, SEED = TRUE, pr = FALSE, 
    fast = FALSE, ...) 
{
    if (SEED) 
        set.seed(2)
    if (is.null(dim(x))) 
        stop("x should be a matrix or data frame with ncol>1")
    if (is.null(dim(y))) 
        stop("y should be a matrix or data frame with ncol>1")
    if (ncol(x) == 1 || ncol(y) == 1) 
        stop("Only multivariate data are allowed")
    n1 <- nrow(x)
    n2 <- nrow(y)
    adalpha <- NA
    if (DF) {
        if (n1 == n2 && alpha == 0.05) {
            p1 <- ncol(x)
            if (p1 == 2) {
                if (n1 >= 20) 
                  adalpha <- 1.36/n1 + 0.05
            }
            if (p1 == 3) {
                if (n1 >= 20) 
                  adalpha <- 1.44/n + 0.05
            }
            if (p1 == 4) {
                if (n1 >= 40) 
                  adalpha <- 2.47/n1 + 0.05
            }
            if (p1 == 5) {
                if (n1 >= 40) 
                  adalpha <- 3.43/n + 0.05
            }
            if (p1 == 6) {
                if (n1 >= 60) 
                  adalpha <- 4.01/n1 + 0.05
            }
        }
    }
    val <- NA
    for (j in 1:nboot) {
        data1 <- sample(n1, size = n1, replace = T)
        data2 <- sample(n2, size = n2, replace = T)
        if (!DF) {
            val[j] <- rgvar(as.matrix(x[data1, ]), est = est, 
                ...) - rgvar(as.matrix(y[data2, ]), est = est, 
                ...)
        }
        if (DF) {
            val[j] <- if (!fast) {
                rgvar(as.matrix(x[data1, ]), est = skipcov, op = op, 
                  outpro.cop = cop, MM = MM, ...) - rgvar(as.matrix(y[data2, 
                  ]), est = skipcov, op = op, outpro.cop = cop, 
                  MM = MM, ...)
            }
            if (fast) {
                rgvar(as.matrix(x[data1, ]), est = skipcov.for, 
                  op = op, outpro.cop = cop, MM = MM, ...) - 
                  rgvar(as.matrix(y[data2, ]), est = skipcov.for, 
                    op = op, outpro.cop = cop, MM = MM, ...)
            }
            if (pr) 
                print(c(j, val[j]))
        }
    }
    p.value <- sum(val < 0)/nboot
    p.value <- 2 * min(p.value, 1 - p.value)
    est1 = rgvar(x, est = est)
    est2 = rgvar(y, est = est)
    list(p.value = p.value, adjusted.crit.level = adalpha, ratio.of.estimates = est1/est2, 
        n1 = n1, n2 = n2)
  }

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