1 |
x |
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y |
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est |
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nboot |
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alpha |
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fr |
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xout |
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outfun |
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com.pval |
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SEED |
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qval |
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... |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ##---- 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, est = tmean, nboot = 500, alpha = 0.05, fr = NA,
xout = TRUE, outfun = outpro, com.pval = FALSE, SEED = TRUE,
qval = 0.5, ...)
{
if (ncol(x) != 2)
stop("There should be two predictors")
temp <- cbind(x, y)
p <- ncol(x)
p1 <- p + 1
temp <- elimna(temp)
x <- temp[, 1:p]
x <- as.matrix(x)
y <- temp[, p1]
if (xout) {
keepit <- rep(T, nrow(x))
flag <- outfun(x, plotit = FALSE, ...)$out.id
keepit[flag] <- FALSE
x <- x[keepit, ]
y <- y[keepit]
}
if (alpha < 0.05 && nboot <= 100)
warning("You used alpha<.05 and nboot<=100")
if (is.na(fr)) {
fr <- 0.8
if (ncol(x) == 2) {
nval <- c(20, 30, 50, 80, 100, 200, 300, 400)
fval <- c(0.4, 0.36, 0.3, 0.25, 0.23, 0.12, 0.08,
0.015)
if (length(y) <= 400)
fr <- approx(nval, fval, length(y))$y
if (length(y) > 400)
fr <- 0.01
}
}
if (SEED)
set.seed(2)
x <- as.matrix(x)
mflag <- matrix(NA, nrow = length(y), ncol = length(y))
for (j in 1:length(y)) {
for (k in 1:length(y)) {
mflag[j, k] <- (sum(x[j, ] <= x[k, ]) == ncol(x))
}
}
yhat <- adrun(x, y, est = est, plotit = FALSE, fr = fr, pyhat = T)
regres <- y - yhat
test2 = medind(regres, x[, 1] * x[, 2], qval = qval, nboot = nboot,
com.pval = com.pval, SEED = SEED, alpha = alpha, pr = TRUE,
xout = xout, outfun = outfun, ...)
test2
}
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