Dancova:

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

Usage

Dancova(x1, y1, x2, y2, fr1 = 1, fr2 = 1, tr = 0.2, alpha = 0.05, plotit = TRUE, pts = NA, sm = FALSE, xout = FALSE, outfun = out, DIF = FALSE, LP = TRUE, xlab = "X", ylab = "Y", pch1 = "*", pch2 = "+", ...)

Arguments

x1
y1
x2
y2
fr1
fr2
tr
alpha
plotit
pts
sm
xout
outfun
DIF
LP
xlab
ylab
pch1
pch2
...

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, fr1 = 1, fr2 = 1, tr = 0.2, alpha = 0.05, 
    plotit = TRUE, pts = NA, sm = FALSE, xout = FALSE, outfun = out, 
    DIF = FALSE, LP = TRUE, xlab = "X", ylab = "Y", pch1 = "*", 
    pch2 = "+", ...) 
{
    if (ncol(as.matrix(x1)) > 1) 
        stop("One covariate only is allowed with this function")
    if (length(x1) != length(y1)) 
        stop("x1 and y1 have different lengths")
    if (length(x1) != length(x2)) 
        stop("x1 and y2 have different lengths")
    if (length(x2) != length(y2)) 
        stop("x2 and y2 have different lengths")
    if (length(y1) != length(y2)) 
        stop("y1 and y2 have different lengths")
    xy = elimna(cbind(x1, y1, x2, y2))
    x1 = xy[, 1]
    y1 = xy[, 2]
    x2 = xy[, 3]
    y2 = xy[, 4]
    xorder <- order(x1)
    y1 <- y1[xorder]
    x1 <- x1[xorder]
    xorder <- order(x2)
    y2 <- y2[xorder]
    x2 <- x2[xorder]
    if (!is.na(pts[1])) 
        mat = Dancovapts(x1, y1, x2, y2, fr1 = fr1, fr2 = fr2, 
            tr = tr, alpha = alpha, plotit = FALSE, pts = pts, 
            sm = sm, xout = xout, outfun = outfun, DIF = DIF, 
            ...)
    if (is.na(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
        n = length(y1)
        ivals = c(1:n)
        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)
        mat <- matrix(NA, 5, 9)
        dimnames(mat) <- list(NULL, c("X", "n", "DIF", "TEST", 
            "se", "ci.low", "ci.hi", "p.value", "p.crit"))
        for (i in 1:5) {
            t1 = near(x1, x1[isub[i]], fr1)
            t2 = near(x2, x1[isub[i]], fr2)
            iv1 = ivals[t1]
            iv2 = ivals[t2]
            pick = unique(c(iv1, iv2))
            mat[i, 2] <- length(y1[pick])
            if (!DIF) 
                test <- yuend(y1[pick], y2[pick], tr = tr, alpha = alpha)
            if (DIF) 
                test <- trimci(y1[pick] - y2[pick], tr = tr, 
                  pr = FALSE, alpha = alpha)
            mat[i, 1] <- x1[isub[i]]
            if (!DIF) {
                mat[i, 4] <- test$teststat
                mat[i, 3] <- test$dif
            }
            if (DIF) {
                mat[i, 4] <- test$test.stat
                mat[i, 3] <- test$estimate
            }
            mat[i, 5] <- test$se
            mat[i, 6] <- test$ci[1]
            mat[i, 7] <- test$ci[2]
            mat[i, 8] <- test$p.value
        }
        temp2 <- order(0 - mat[, 8])
        bot = c(1:nrow(mat))
        dvec = sort(alpha/bot, decreasing = TRUE)
        mat[temp2, 9] = dvec
    }
    if (plotit) {
        if (xout) {
            flag <- outfun(x1, ...)$keep
            x1 <- x1[flag]
            y1 <- y1[flag]
            flag <- outfun(x2, ...)$keep
            x2 <- x2[flag]
            y2 <- y2[flag]
        }
        runmean2g(x1, y1, x2, y2, fr = fr1, est = tmean, sm = sm, 
            xout = FALSE, LP = LP, xlab = xlab, ylab = ylab, 
            pch1 = pch1, pch2 = pch2, ...)
    }
    list(output = mat)
  }

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