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In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x1
y1
x2
y2
fr1
fr2
alpha
plotit
pts
xout
outfun
LP
sm
est
...
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, alpha = 5, plotit = TRUE,
pts = NA, xout = FALSE, outfun = out, LP = TRUE, sm = FALSE,
est = hd, ...)
{
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 (xout) {
flag <- outfun(x1, ...)$keep
x1 <- x1[flag]
y1 <- y1[flag]
flag <- outfun(x2, ...)$keep
x2 <- x2[flag]
y2 <- y2[flag]
}
dv.sum = NULL
if (is.na(pts[1])) {
npt <- 5
CC = 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, 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "p.hat",
"ci.low", "ci.hi", "p.value", "p.crit"))
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 <- cidv2(g1, g2)
dv.sum = rbind(dv.sum, test$summary.dvals)
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(g1)
mat[i, 3] <- length(g2)
mat[i, 4] <- test$p.hat
mat[i, 5] <- test$p.ci[1]
mat[i, 6] <- test$p.ci[2]
mat[i, 7] <- test$p.value
}
}
if (!is.na(pts[1])) {
CC = length(pts)
n1 <- 1
n2 <- 1
vecn <- 1
for (i in 1:length(pts)) {
n1[i] <- length(y1[near(x1, pts[i], fr1)])
n2[i] <- length(y2[near(x2, pts[i], fr2)])
}
mat <- matrix(NA, length(pts), 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "p.hat",
"ci.low", "ci.hi", "p.value", "p.crit"))
for (i in 1:length(pts)) {
g1 <- y1[near(x1, pts[i], fr1)]
g2 <- y2[near(x2, pts[i], fr2)]
g1 <- g1[!is.na(g1)]
g2 <- g2[!is.na(g2)]
test = cidv2(g1, g2)
dv.sum = rbind(dv.sum, test$summary.dvals)
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$p.hat
mat[i, 5] <- test$p.ci[1]
mat[i, 6] <- test$p.ci[2]
mat[i, 7] <- test$p.value
}
}
dvec <- alpha/c(1:CC)
temp2 <- order(0 - mat[, 6])
mat[temp2, 8] = dvec
if (plotit) {
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
xout = FALSE, LP = LP, ...)
}
list(output = mat, summary = dv.sum)
}
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 |
|
alpha |
|
plotit |
|
pts |
|
xout |
|
outfun |
|
LP |
|
sm |
|
est |
|
... |
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 | ##---- 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, alpha = 5, plotit = TRUE,
pts = NA, xout = FALSE, outfun = out, LP = TRUE, sm = FALSE,
est = hd, ...)
{
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 (xout) {
flag <- outfun(x1, ...)$keep
x1 <- x1[flag]
y1 <- y1[flag]
flag <- outfun(x2, ...)$keep
x2 <- x2[flag]
y2 <- y2[flag]
}
dv.sum = NULL
if (is.na(pts[1])) {
npt <- 5
CC = 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, 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "p.hat",
"ci.low", "ci.hi", "p.value", "p.crit"))
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 <- cidv2(g1, g2)
dv.sum = rbind(dv.sum, test$summary.dvals)
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(g1)
mat[i, 3] <- length(g2)
mat[i, 4] <- test$p.hat
mat[i, 5] <- test$p.ci[1]
mat[i, 6] <- test$p.ci[2]
mat[i, 7] <- test$p.value
}
}
if (!is.na(pts[1])) {
CC = length(pts)
n1 <- 1
n2 <- 1
vecn <- 1
for (i in 1:length(pts)) {
n1[i] <- length(y1[near(x1, pts[i], fr1)])
n2[i] <- length(y2[near(x2, pts[i], fr2)])
}
mat <- matrix(NA, length(pts), 8)
dimnames(mat) <- list(NULL, c("X", "n1", "n2", "p.hat",
"ci.low", "ci.hi", "p.value", "p.crit"))
for (i in 1:length(pts)) {
g1 <- y1[near(x1, pts[i], fr1)]
g2 <- y2[near(x2, pts[i], fr2)]
g1 <- g1[!is.na(g1)]
g2 <- g2[!is.na(g2)]
test = cidv2(g1, g2)
dv.sum = rbind(dv.sum, test$summary.dvals)
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$p.hat
mat[i, 5] <- test$p.ci[1]
mat[i, 6] <- test$p.ci[2]
mat[i, 7] <- test$p.value
}
}
dvec <- alpha/c(1:CC)
temp2 <- order(0 - mat[, 6])
mat[temp2, 8] = dvec
if (plotit) {
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
xout = FALSE, LP = LP, ...)
}
list(output = mat, summary = dv.sum)
}
|
R Package Documentation
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