ancbbmed:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x1
y1
x2
y2
fr1
fr2
nboot
pts
plotit
SEED
alpha
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 (x1, y1, x2, y2, fr1 = 1, fr2 = 1, nboot = 100, pts = NA,
plotit = TRUE, SEED = TRUE, alpha = 0.05)
{
if (SEED)
set.seed(2)
if (is.na(pts[1])) {
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, 7)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value"))
gv1 <- vector("list")
for (i in 1:5) {
j <- i + 5
temp1 <- y1[near(x1, x1[isub[i]], fr1)]
temp2 <- y2[near(x2, x1[isub[i]], fr2)]
temp1 <- temp1[!is.na(temp1)]
temp2 <- temp2[!is.na(temp2)]
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(temp1)
mat[i, 3] <- length(temp2)
mat[, 4] <- runmbo(x1, y1, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = tmean) -
runmbo(x2, y2, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = median)
gv1[[i]] <- temp1
gv1[[j]] <- temp2
}
I1 <- diag(5)
I2 <- 0 - I1
con <- rbind(I1, I2)
estmat1 <- matrix(nrow = nboot, ncol = length(isub))
estmat2 <- matrix(nrow = nboot, ncol = length(isub))
data1 <- matrix(sample(length(y1), size = length(y1) *
nboot, replace = TRUE), nrow = nboot)
data2 <- matrix(sample(length(y2), size = length(y2) *
nboot, replace = TRUE), nrow = nboot)
for (ib in 1:nboot) {
estmat1[ib, ] = runmbo(x1[data1[ib, ]], y1[data1[ib,
]], pts = x1[isub], pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = x1[isub], pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
}
dif <- (estmat1 < estmat2)
dif0 <- (estmat1 == estmat2)
pvals = apply(dif, 2, mean, na.rm = TRUE) + 0.5 * apply(dif0,
2, mean, na.rm = TRUE)
tmat <- rbind(pvals, 1 - pvals)
pvals = 2 * apply(tmat, 2, min)
mat[, 7] <- pvals
for (ij in 1:length(isub)) {
dif <- estmat1[, ij] - estmat2[, ij]
dif <- elimna(dif)
nbad <- length(dif)
lo <- round(nbad * alpha/2)
hi <- nbad - lo
dif <- sort(dif)
mat[ij, 5] <- dif[lo]
mat[ij, 6] <- dif[hi]
}
}
if (!is.na(pts[1])) {
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)])
if (n1[i] <= 5)
print(paste("Warning, there are", n1[i], " points corresponding to the design point X=",
pts[i]))
if (n2[i] <= 5)
print(paste("Warning, there are", n2[i], " points corresponding to the design point X=",
pts[i]))
}
mat <- matrix(NA, length(pts), 7)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value"))
gv <- vector("list", 2 * length(pts))
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)]
j <- i + length(pts)
gv[[i]] <- g1
gv[[j]] <- g2
}
I1 <- diag(length(pts))
I2 <- 0 - I1
con <- rbind(I1, I2)
isub = c(1:length(pts))
estmat1 <- matrix(nrow = nboot, ncol = length(isub))
estmat2 <- matrix(nrow = nboot, ncol = length(isub))
data1 <- matrix(sample(length(y1), size = length(y1) *
nboot, replace = TRUE), nrow = nboot)
data2 <- matrix(sample(length(y2), size = length(y2) *
nboot, replace = TRUE), nrow = nboot)
est1 = runmbo(x1, y1, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
est2 = runmbo(x2, y2, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
mat[, 4] <- est1 - est2
for (ib in 1:nboot) {
estmat1[ib, ] = runmbo(x1[data1[ib, ]], y1[data1[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
}
dif <- (estmat1 < estmat2)
dif0 <- (estmat1 == estmat2)
pvals = apply(dif, 2, mean, na.rm = TRUE) + 0.5 * apply(dif0,
2, mean, na.rm = TRUE)
tmat <- rbind(pvals, 1 - pvals)
pvals = 2 * apply(tmat, 2, min)
mat[, 1] <- pts
mat[, 2] <- n1
mat[, 3] <- n2
mat[, 7] <- pvals
for (ij in 1:length(pts)) {
dif <- sort(estmat1[, ij] - estmat2[, ij])
dif <- elimna(dif)
nbad <- length(dif)
lo <- round(nbad * alpha/2)
hi <- nbad - lo
mat[ij, 5] <- dif[lo]
mat[ij, 6] <- dif[hi]
}
}
if (plotit)
runmean2g(x1, y1, x2, y2, fr = fr1, est = median, sm = T)
list(output = mat)
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
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Usage
1 |
Arguments
x1 |
|
y1 |
|
x2 |
|
y2 |
|
fr1 |
|
fr2 |
|
nboot |
|
pts |
|
plotit |
|
SEED |
|
alpha |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | ##---- 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, nboot = 100, pts = NA,
plotit = TRUE, SEED = TRUE, alpha = 0.05)
{
if (SEED)
set.seed(2)
if (is.na(pts[1])) {
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, 7)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value"))
gv1 <- vector("list")
for (i in 1:5) {
j <- i + 5
temp1 <- y1[near(x1, x1[isub[i]], fr1)]
temp2 <- y2[near(x2, x1[isub[i]], fr2)]
temp1 <- temp1[!is.na(temp1)]
temp2 <- temp2[!is.na(temp2)]
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(temp1)
mat[i, 3] <- length(temp2)
mat[, 4] <- runmbo(x1, y1, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = tmean) -
runmbo(x2, y2, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = median)
gv1[[i]] <- temp1
gv1[[j]] <- temp2
}
I1 <- diag(5)
I2 <- 0 - I1
con <- rbind(I1, I2)
estmat1 <- matrix(nrow = nboot, ncol = length(isub))
estmat2 <- matrix(nrow = nboot, ncol = length(isub))
data1 <- matrix(sample(length(y1), size = length(y1) *
nboot, replace = TRUE), nrow = nboot)
data2 <- matrix(sample(length(y2), size = length(y2) *
nboot, replace = TRUE), nrow = nboot)
for (ib in 1:nboot) {
estmat1[ib, ] = runmbo(x1[data1[ib, ]], y1[data1[ib,
]], pts = x1[isub], pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = x1[isub], pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
}
dif <- (estmat1 < estmat2)
dif0 <- (estmat1 == estmat2)
pvals = apply(dif, 2, mean, na.rm = TRUE) + 0.5 * apply(dif0,
2, mean, na.rm = TRUE)
tmat <- rbind(pvals, 1 - pvals)
pvals = 2 * apply(tmat, 2, min)
mat[, 7] <- pvals
for (ij in 1:length(isub)) {
dif <- estmat1[, ij] - estmat2[, ij]
dif <- elimna(dif)
nbad <- length(dif)
lo <- round(nbad * alpha/2)
hi <- nbad - lo
dif <- sort(dif)
mat[ij, 5] <- dif[lo]
mat[ij, 6] <- dif[hi]
}
}
if (!is.na(pts[1])) {
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)])
if (n1[i] <= 5)
print(paste("Warning, there are", n1[i], " points corresponding to the design point X=",
pts[i]))
if (n2[i] <= 5)
print(paste("Warning, there are", n2[i], " points corresponding to the design point X=",
pts[i]))
}
mat <- matrix(NA, length(pts), 7)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value"))
gv <- vector("list", 2 * length(pts))
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)]
j <- i + length(pts)
gv[[i]] <- g1
gv[[j]] <- g2
}
I1 <- diag(length(pts))
I2 <- 0 - I1
con <- rbind(I1, I2)
isub = c(1:length(pts))
estmat1 <- matrix(nrow = nboot, ncol = length(isub))
estmat2 <- matrix(nrow = nboot, ncol = length(isub))
data1 <- matrix(sample(length(y1), size = length(y1) *
nboot, replace = TRUE), nrow = nboot)
data2 <- matrix(sample(length(y2), size = length(y2) *
nboot, replace = TRUE), nrow = nboot)
est1 = runmbo(x1, y1, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
est2 = runmbo(x2, y2, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
mat[, 4] <- est1 - est2
for (ib in 1:nboot) {
estmat1[ib, ] = runmbo(x1[data1[ib, ]], y1[data1[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = median)
}
dif <- (estmat1 < estmat2)
dif0 <- (estmat1 == estmat2)
pvals = apply(dif, 2, mean, na.rm = TRUE) + 0.5 * apply(dif0,
2, mean, na.rm = TRUE)
tmat <- rbind(pvals, 1 - pvals)
pvals = 2 * apply(tmat, 2, min)
mat[, 1] <- pts
mat[, 2] <- n1
mat[, 3] <- n2
mat[, 7] <- pvals
for (ij in 1:length(pts)) {
dif <- sort(estmat1[, ij] - estmat2[, ij])
dif <- elimna(dif)
nbad <- length(dif)
lo <- round(nbad * alpha/2)
hi <- nbad - lo
mat[ij, 5] <- dif[lo]
mat[ij, 6] <- dif[hi]
}
}
if (plotit)
runmean2g(x1, y1, x2, y2, fr = fr1, est = median, sm = T)
list(output = mat)
}
|
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