Dancols:

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

Usage

Dancols(x1, y1, x2, y2, pts = NULL, fr1 = 1, fr2 = 1, alpha = 0.05, plotit = TRUE, xout = FALSE, outfun = out, nboot = 100, SEED = TRUE, xlab = "X", ylab = "Y", CR = FALSE, ...)

Arguments

x1
y1
x2
y2
pts
fr1
fr2
alpha
plotit
xout
outfun
nboot
SEED
xlab
ylab
CR
...

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 (x1, y1, x2, y2, pts = NULL, fr1 = 1, fr2 = 1, alpha = 0.05, 
    plotit = TRUE, xout = FALSE, outfun = out, nboot = 100, SEED = TRUE, 
    xlab = "X", ylab = "Y", CR = FALSE, ...) 
{
    x1 = as.matrix(x1)
    x2 = as.matrix(x2)
    if (ncol(x1) != ncol(x2)) 
        stop("x1 and x2 have different number of columns")
    if (SEED) 
        set.seed(2)
    FLAG = pts
    X = elimna(cbind(x1, y1, x2, y2))
    if (ncol(X) > 4) 
        stop("Only one covariate is allowed")
    x1 = as.matrix(x1)
    x2 = as.matrix(x2)
    p = ncol(x1)
    p1 = p + 1
    p2 = p + 2
    p3 = p1 + p
    p4 = p3 + 1
    x1 = X[, 1:p]
    y1 = X[, p1]
    x2 = X[, p2:p3]
    y2 = X[, p4]
    n = length(y1)
    if (xout) {
        flag1 = outfun(x1, SEED = SEED, ...)$out.id
        flag2 = outfun(x2, SEED = SEED, ...)$out.id
        flag = unique(c(flag1, flag2))
        if (length(flag) > 0) 
            X = X[-flag, ]
        x1 = X[, 1:p]
        y1 = X[, p1]
        x2 = X[, p2:p3]
        y2 = X[, p4]
    }
    n.keep = length(y1)
    if (is.null(pts[1])) {
        npt <- 5
        isub <- c(1:5)
        test <- c(1:5)
        xorder <- order(x1)
        y1 <- y1[xorder]
        x1 <- x1[xorder]
        xorder <- order(x2)
        y2 <- y2[xorder]
        x2 <- x2[xorder]
        n1 <- 1
        n2 <- 1
        vecn <- 1
        for (i in 1:length(x1)) n1[i] <- length(y1[near(x1, x1[i], 
            fr1)])
        for (i in 1:length(x1)) n2[i] <- length(y2[near(x2, x1[i], 
            fr2)])
        for (i in 1:length(x1)) vecn[i] <- min(n1[i], n2[i])
        sub <- c(1:length(x1))
        isub[1] <- min(sub[vecn >= 12])
        isub[5] <- max(sub[vecn >= 12])
        isub[3] <- floor((isub[1] + isub[5])/2)
        isub[2] <- floor((isub[1] + isub[3])/2)
        isub[4] <- floor((isub[3] + isub[5])/2)
        pts = x1[isub]
        pts = unique(pts)
        npt = nrow(as.matrix(pts))
        mat <- matrix(NA, npt, 9)
        dimnames(mat) <- list(NULL, c("X", "Est1", "Est2", "DIF", 
            "TEST", "se", "ci.low", "ci.hi", "p.value"))
        mat[, 1] = pts
        sqsd = difregYvar(x1, y1, x2, y2, regfun = lsfit, pts = pts, 
            nboot = nboot, SEED = SEED)
        est1 = regYhat(x1, y1, xr = pts, regfun = lsfit)
        est2 = regYhat(x2, y2, xr = pts, regfun = lsfit)
        mat[, 2] = est1
        mat[, 3] = est2
        est = est1 - est2
        mat[, 4] = est
        sd = sqrt(sqsd)
        mat[, 6] = sd
        tests = (est1 - est2)/sd
        mat[, 5] = tests
        df = length(y1) - 1
        pv = 2 * (1 - pt(abs(tests), df))
        mat[, 9] = pv
        crit <- smmcrit(df, 5)
        mat[, 7] = est - crit * sd
        mat[, 8] = est + crit * sd
    }
    if (!is.null(FLAG)) {
        for (i in 1:length(pts)) {
            n1[i] <- length(y1[near(x1, pts[i], fr1)])
            n2[i] <- length(y2[near(x2, pts[i], fr2)])
        }
        pts = unique(pts)
        mat <- matrix(NA, length(pts), 9)
        dimnames(mat) <- list(NULL, c("X", "Est1", "Est2", "DIF", 
            "TEST", "se", "ci.low", "ci.hi", "p.value"))
        mat[, 1] <- pts
        sqsd = difregYvar(x1, y1, x2, y2, regfun = lsfit, pts = pts, 
            nboot = nboot, SEED = SEED)
        est1 = regYhat(x1, y1, xr = pts, regfun = lsfit, , ...)
        est2 = regYhat(x2, y2, xr = pts, regfun = lsfit, , ...)
        mat[, 2] = est1
        mat[, 3] = est2
        est = est1 - est2
        mat[, 4] = est
        sd = sqrt(sqsd)
        mat[, 6] = sd
        tests = (est1 - est2)/sd
        mat[, 5] = tests
        df = length(y1) - 1
        pv = 2 * (1 - pt(abs(tests), df))
        mat[, 9] = pv
        crit <- smmcrit(df, length(pts))
        mat[, 7] = est - crit * sd
        mat[, 8] = est + crit * sd
    }
    if (plotit) {
        plot(c(x1, x2), c(y1, y2), type = "n", xlab = xlab, ylab = ylab)
        points(x1, y1, pch = "o")
        points(x2, y2, pch = "+")
        abline(lsfit(x1, y1)$coef)
        abline(lsfit(x2, y2)$coef, lty = 2)
    }
    int = NULL
    crq = NULL
    crq2 = NULL
    if (CR) {
        if (ncol(as.matrix(x1)) > 1) 
            stop("CR=T only allowed with one covariate")
        int = DancCR(x1, y1, x2, y2)
        crq = mean(x1 <= int[1])
        crq[2] = mean(x1 <= int[2])
        crq2 = mean(x2 <= int[1])
        crq2[2] = mean(x2 <= int[2])
    }
    list(n = n, n.keep = n.keep, output = mat, cross.interval = int, 
        cr.quant.grp1 = crq, cr.quant.grp2 = crq2)
  }

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