ancova:

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

Usage

ancova(x1, y1, x2, y2, fr1 = 1, fr2 = 1, tr = 0.2, alpha = 0.05, plotit = TRUE, pts = NA, sm = FALSE, pr = TRUE, xout = FALSE, outfun = out, LP = TRUE, SCAT = TRUE, xlab = "X", ylab = "Y", pch1 = "*", pch2 = "+", skip.crit = FALSE, crit.val = 1.09, ...)

Arguments

x1
y1
x2
y2
fr1
fr2
tr
alpha
plotit
pts
sm
pr
xout
outfun
LP
SCAT
xlab
ylab
pch1
pch2
skip.crit
crit.val
...

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, pr = TRUE, xout = FALSE, 
    outfun = out, LP = TRUE, SCAT = TRUE, xlab = "X", ylab = "Y", 
    pch1 = "*", pch2 = "+", skip.crit = FALSE, crit.val = 1.09, 
    ...) 
{
    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(x2) != length(y2)) 
        stop("x2 and y2 have different lengths")
    xy = elimna(cbind(x1, y1))
    x1 = xy[, 1]
    y1 = xy[, 2]
    xy = elimna(cbind(x2, y2))
    x2 = xy[, 1]
    y2 = xy[, 2]
    if (pr) {
        print("NOTE: Confidence intervals are adjusted to control the probability")
        print("of at least one Type I error.")
        print("But p-values are not")
    }
    if (xout) {
        flag <- outfun(x1, ...)$keep
        x1 <- x1[flag]
        y1 <- y1[flag]
        flag <- outfun(x2, ...)$keep
        x2 <- x2[flag]
        y2 <- y2[flag]
    }
    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
        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, 10)
        dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF", 
            "TEST", "se", "ci.low", "ci.hi", "p.value", "crit.val"))
        for (i in 1:5) {
            g1 <- y1[near(x1, x1[isub[i]], fr1)]
            g2 <- y2[near(x2, x1[isub[i]], fr2)]
            g1 <- g1[!is.na(g1)]
            g2 <- g2[!is.na(g2)]
            test <- yuen(g1, g2, tr = tr)
            mat[i, 1] <- x1[isub[i]]
            mat[i, 2] <- length(g1)
            mat[i, 3] <- length(g2)
            mat[i, 4] <- test$dif
            mat[i, 5] <- test$teststat
            mat[i, 6] <- test$se
            if (skip.crit) 
                critv = crit.val
            if (!skip.crit) {
                critv <- NA
                if (alpha == 0.05) 
                  critv <- smmcrit(test$df, 5)
                if (alpha == 0.01) 
                  critv <- smmcrit01(test$df, 5)
                if (is.na(critv)) 
                  critv <- smmval(test$df, 5, alpha = alpha)
                mat[i, 10] <- critv
            }
            cilow <- test$dif - critv * test$se
            cihi <- test$dif + critv * test$se
            mat[i, 7] <- cilow
            mat[i, 8] <- cihi
            mat[i, 9] <- test$p.value
        }
    }
    if (!is.na(pts[1])) {
        if (!skip.crit) {
            if (length(pts) >= 29) 
                stop("At most 28 points can be compared")
        }
        n1 <- 1
        n2 <- 1
        vecn <- 1
        for (i in 1:length(pts)) {
            n1[i] <- length(y1[near(x1, pts[i], fr1)])
            n2[i] <- length(y2[near(x2, pts[i], fr2)])
        }
        mat <- matrix(NA, length(pts), 10)
        dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF", 
            "TEST", "se", "ci.low", "ci.hi", "p.value", "crit.val"))
        for (i in 1:length(pts)) {
            g1 <- y1[near(x1, pts[i], fr1)]
            g2 <- y2[near(x2, pts[i], fr2)]
            g1 <- g1[!is.na(g1)]
            g2 <- g2[!is.na(g2)]
            test <- yuen(g1, g2, tr = tr)
            mat[i, 1] <- pts[i]
            mat[i, 2] <- length(g1)
            mat[i, 3] <- length(g2)
            if (length(g1) <= 5) 
                print(paste("Warning, there are", length(g1), 
                  " points corresponding to the design point X=", 
                  pts[i]))
            if (length(g2) <= 5) 
                print(paste("Warning, there are", length(g2), 
                  " points corresponding to the design point X=", 
                  pts[i]))
            mat[i, 4] <- test$dif
            mat[i, 5] <- test$teststat
            mat[i, 6] <- test$se
            if (skip.crit) 
                critv = crit.val
            if (!skip.crit) {
                if (length(pts) >= 2) 
                  critv <- smmcrit(test$df, length(pts))
                if (length(pts) == 1) 
                  critv <- qt(0.975, test$df)
            }
            cilow <- test$dif - critv * test$se
            cihi <- test$dif + critv * test$se
            mat[i, 7] <- cilow
            mat[i, 8] <- cihi
            mat[i, 9] <- test$p.value
            mat[i, 10] <- critv
        }
    }
    if (plotit) {
        runmean2g(x1, y1, x2, y2, fr = fr1, est = mean, tr = tr, 
            sm = sm, xout = FALSE, LP = LP, SCAT = SCAT, xlab = xlab, 
            ylab = ylab, pch1 = pch1, pch2 = pch2, ...)
    }
    list(output = mat)
  }

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