lsqs2:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x
y
MD
tr
plotit
Examples
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##---- 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, MD = FALSE, tr = 0.05, plotit = TRUE)
{
if (is.list(x))
x <- matl(x)
if (is.list(y))
y <- matl(y)
disyx <- NA
disxy <- NA
if (!is.matrix(x) && !is.matrix(y)) {
x <- x[!is.na(x)]
y <- y[!is.na(y)]
tempxx <- NA
for (i in 1:length(x)) {
tempxx[i] <- sum(x[i] <= x)/length(x)
if (tempxx[i] > 0.5)
tempxx[i] <- 1 - tempxx[i]
}
for (i in 1:length(x)) {
temp <- sum(x[i] <= y)/length(y)
if (temp > 0.5)
temp <- 1 - temp
disxy[i] <- mean(temp > tempxx)
}
tempyy <- NA
for (i in 1:length(y)) {
tempyy[i] <- sum(y[i] <= y)/length(y)
if (tempyy[i] > 0.5)
tempyy[i] <- 1 - tempyy[i]
}
for (i in 1:length(y)) {
temp <- sum(y[i] <= x)/length(x)
if (temp > 0.5)
temp <- 1 - temp
disyx[i] <- mean(temp > tempyy)
}
qhatxy <- mean(disyx)
qhatyx <- mean(disxy)
qhat <- (qhatxy + qhatyx)/2
}
if (is.matrix(x) && is.matrix(x)) {
if (!MD) {
if (ncol(x) != 2 || ncol(y) != 2) {
tempyy <- fdepth(y)
temp <- fdepth(y, x)
for (i in 1:nrow(x)) {
disxy[i] <- mean(temp[i] > tempyy)
}
tempxx <- NA
tempxx <- fdepth(x)
temp <- fdepth(x, pts = y)
for (i in 1:nrow(y)) {
disyx[i] <- mean(temp[i] > tempxx)
}
}
if (ncol(x) == 2 && ncol(y) == 2) {
if (plotit) {
plot(rbind(x, y), type = "n", xlab = "Var 1",
ylab = "VAR 2")
points(x)
points(y, pch = "o")
temp <- NA
for (i in 1:nrow(x)) {
temp[i] <- depth(x[i, 1], x[i, 2], x)
}
flag <- (temp >= median(temp))
xx <- x[flag, ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ])
lines(xx[c(temp[1], temp[length(temp)]), ])
temp <- NA
for (i in 1:nrow(y)) {
temp[i] <- depth(y[i, 1], y[i, 2], y)
}
flag <- (temp >= median(temp))
xx <- y[flag, ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
flag <- (temp >= median(temp))
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ], lty = 2)
lines(xx[c(temp[1], temp[length(temp)]), ],
lty = 2)
}
tempyy <- NA
for (i in 1:nrow(y)) tempyy[i] <- depth(y[i,
1], y[i, 2], y)
for (i in 1:nrow(x)) {
temp <- depth(x[i, 1], x[i, 2], y)
disxy[i] <- mean(temp > tempyy)
}
tempxx <- NA
for (i in 1:nrow(x)) tempxx[i] <- depth(x[i,
1], x[i, 2], x)
for (i in 1:nrow(y)) {
temp <- depth(y[i, 1], y[i, 2], x)
disyx[i] <- mean(temp > tempxx)
}
}
}
if (MD) {
mx <- apply(x, 2, median)
my <- apply(y, 2, median)
vx <- apply(x, 2, winval, tr = tr) - apply(x, 2,
mean, trim = tr) + mx
vx <- var(vx)
vy <- apply(y, 2, winval, tr = tr) - apply(y, 2,
mean, trim = tr) + my
vy <- var(vy)
tempxx <- 1/(1 + mahalanobis(x, mx, vx))
tempyx <- 1/(1 + mahalanobis(y, mx, vx))
for (i in 1:nrow(y)) {
disyx[i] <- mean(tempyx[i] > tempxx)
}
tempyy <- 1/(1 + mahalanobis(y, my, vy))
tempxy <- 1/(1 + mahalanobis(x, my, vy))
for (i in 1:nrow(x)) {
disxy[i] <- mean(tempxy[i] > tempyy)
}
}
qhatxy <- sum(disxy)
qhatyx <- sum(disyx)
qhat <- (qhatxy + qhatyx)/(length(disxy) + length(disyx))
}
qhatyx <- mean(disyx)
qhatxy <- mean(disxy)
list(qhatxy, qhatyx, qhat)
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
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Usage
1 |
Arguments
x |
|
y |
|
MD |
|
tr |
|
plotit |
Examples
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | ##---- 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, MD = FALSE, tr = 0.05, plotit = TRUE)
{
if (is.list(x))
x <- matl(x)
if (is.list(y))
y <- matl(y)
disyx <- NA
disxy <- NA
if (!is.matrix(x) && !is.matrix(y)) {
x <- x[!is.na(x)]
y <- y[!is.na(y)]
tempxx <- NA
for (i in 1:length(x)) {
tempxx[i] <- sum(x[i] <= x)/length(x)
if (tempxx[i] > 0.5)
tempxx[i] <- 1 - tempxx[i]
}
for (i in 1:length(x)) {
temp <- sum(x[i] <= y)/length(y)
if (temp > 0.5)
temp <- 1 - temp
disxy[i] <- mean(temp > tempxx)
}
tempyy <- NA
for (i in 1:length(y)) {
tempyy[i] <- sum(y[i] <= y)/length(y)
if (tempyy[i] > 0.5)
tempyy[i] <- 1 - tempyy[i]
}
for (i in 1:length(y)) {
temp <- sum(y[i] <= x)/length(x)
if (temp > 0.5)
temp <- 1 - temp
disyx[i] <- mean(temp > tempyy)
}
qhatxy <- mean(disyx)
qhatyx <- mean(disxy)
qhat <- (qhatxy + qhatyx)/2
}
if (is.matrix(x) && is.matrix(x)) {
if (!MD) {
if (ncol(x) != 2 || ncol(y) != 2) {
tempyy <- fdepth(y)
temp <- fdepth(y, x)
for (i in 1:nrow(x)) {
disxy[i] <- mean(temp[i] > tempyy)
}
tempxx <- NA
tempxx <- fdepth(x)
temp <- fdepth(x, pts = y)
for (i in 1:nrow(y)) {
disyx[i] <- mean(temp[i] > tempxx)
}
}
if (ncol(x) == 2 && ncol(y) == 2) {
if (plotit) {
plot(rbind(x, y), type = "n", xlab = "Var 1",
ylab = "VAR 2")
points(x)
points(y, pch = "o")
temp <- NA
for (i in 1:nrow(x)) {
temp[i] <- depth(x[i, 1], x[i, 2], x)
}
flag <- (temp >= median(temp))
xx <- x[flag, ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ])
lines(xx[c(temp[1], temp[length(temp)]), ])
temp <- NA
for (i in 1:nrow(y)) {
temp[i] <- depth(y[i, 1], y[i, 2], y)
}
flag <- (temp >= median(temp))
xx <- y[flag, ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
flag <- (temp >= median(temp))
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ], lty = 2)
lines(xx[c(temp[1], temp[length(temp)]), ],
lty = 2)
}
tempyy <- NA
for (i in 1:nrow(y)) tempyy[i] <- depth(y[i,
1], y[i, 2], y)
for (i in 1:nrow(x)) {
temp <- depth(x[i, 1], x[i, 2], y)
disxy[i] <- mean(temp > tempyy)
}
tempxx <- NA
for (i in 1:nrow(x)) tempxx[i] <- depth(x[i,
1], x[i, 2], x)
for (i in 1:nrow(y)) {
temp <- depth(y[i, 1], y[i, 2], x)
disyx[i] <- mean(temp > tempxx)
}
}
}
if (MD) {
mx <- apply(x, 2, median)
my <- apply(y, 2, median)
vx <- apply(x, 2, winval, tr = tr) - apply(x, 2,
mean, trim = tr) + mx
vx <- var(vx)
vy <- apply(y, 2, winval, tr = tr) - apply(y, 2,
mean, trim = tr) + my
vy <- var(vy)
tempxx <- 1/(1 + mahalanobis(x, mx, vx))
tempyx <- 1/(1 + mahalanobis(y, mx, vx))
for (i in 1:nrow(y)) {
disyx[i] <- mean(tempyx[i] > tempxx)
}
tempyy <- 1/(1 + mahalanobis(y, my, vy))
tempxy <- 1/(1 + mahalanobis(x, my, vy))
for (i in 1:nrow(x)) {
disxy[i] <- mean(tempxy[i] > tempyy)
}
}
qhatxy <- sum(disxy)
qhatyx <- sum(disyx)
qhat <- (qhatxy + qhatyx)/(length(disxy) + length(disyx))
}
qhatyx <- mean(disyx)
qhatxy <- mean(disxy)
list(qhatxy, qhatyx, qhat)
}
|
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