ancbbpb:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x1
y1
x2
y2
fr1
est
fr2
nboot
pts
plotit
SCAT
pch1
pch2
SEED
alpha
RNA
sm
LP
xout
outfun
...
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, est = tmean, fr2 = 1, nboot = 200,
pts = NA, plotit = TRUE, SCAT = TRUE, pch1 = "+", pch2 = "o",
SEED = TRUE, alpha = 0.05, RNA = T, sm = FALSE, LP = TRUE,
xout = FALSE, outfun = outpro, ...)
{
xy = elimna(cbind(x1, y1))
x1 = xy[, 1]
y1 = xy[, 2]
xy = elimna(cbind(x2, y2))
x2 = xy[, 1]
y2 = xy[, 2]
if (xout) {
flag <- outfun(x1, ...)$keep
x1 <- x1[flag]
y1 <- y1[flag]
flag <- outfun(x2, ...)$keep
x2 <- x2[flag]
y2 <- y2[flag]
}
if (SEED)
set.seed(2)
flag = TRUE
if (is.na(pts[1])) {
flag = FALSE
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, 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value", "p.crit"))
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 = est, RNA = RNA) -
runmbo(x2, y2, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = est, RNA = RNA)
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 = est, ...)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = x1[isub], pyhat = T, plotit = FALSE,
SEED = FALSE, est = est, ...)
}
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 = est, ...)
est2 = runmbo(x2, y2, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = est, ...)
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 = est, ...)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = est, ...)
}
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]
}
}
temp2 <- order(0 - pvals)
zvec = alpha/c(1:length(pvals))
if (flag)
mat[temp2, 7] = zvec
if (!flag)
mat[temp2, 8] = zvec
if (plotit)
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
LP = LP, xout = FALSE, SCAT = SCAT, pch1 = pch1,
pch2 = pch2, ...)
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 |
|
est |
|
fr2 |
|
nboot |
|
pts |
|
plotit |
|
SCAT |
|
pch1 |
|
pch2 |
|
SEED |
|
alpha |
|
RNA |
|
sm |
|
LP |
|
xout |
|
outfun |
|
... |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | ##---- 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, est = tmean, fr2 = 1, nboot = 200,
pts = NA, plotit = TRUE, SCAT = TRUE, pch1 = "+", pch2 = "o",
SEED = TRUE, alpha = 0.05, RNA = T, sm = FALSE, LP = TRUE,
xout = FALSE, outfun = outpro, ...)
{
xy = elimna(cbind(x1, y1))
x1 = xy[, 1]
y1 = xy[, 2]
xy = elimna(cbind(x2, y2))
x2 = xy[, 1]
y2 = xy[, 2]
if (xout) {
flag <- outfun(x1, ...)$keep
x1 <- x1[flag]
y1 <- y1[flag]
flag <- outfun(x2, ...)$keep
x2 <- x2[flag]
y2 <- y2[flag]
}
if (SEED)
set.seed(2)
flag = TRUE
if (is.na(pts[1])) {
flag = FALSE
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, 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "DIF",
"ci.low", "ci.hi", "p.value", "p.crit"))
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 = est, RNA = RNA) -
runmbo(x2, y2, pts = x1[isub], pyhat = TRUE,
plotit = FALSE, SEED = FALSE, est = est, RNA = RNA)
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 = est, ...)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = x1[isub], pyhat = T, plotit = FALSE,
SEED = FALSE, est = est, ...)
}
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 = est, ...)
est2 = runmbo(x2, y2, pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = est, ...)
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 = est, ...)
estmat2[ib, ] = runmbo(x2[data2[ib, ]], y2[data2[ib,
]], pts = pts, pyhat = TRUE, plotit = FALSE,
SEED = FALSE, est = est, ...)
}
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]
}
}
temp2 <- order(0 - pvals)
zvec = alpha/c(1:length(pvals))
if (flag)
mat[temp2, 7] = zvec
if (!flag)
mat[temp2, 8] = zvec
if (plotit)
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
LP = LP, xout = FALSE, SCAT = SCAT, pch1 = pch1,
pch2 = pch2, ...)
list(output = mat)
}
|
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