Dancovapb:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x1
y1
x2
y2
fr1
fr2
est
alpha
plotit
pts
sm
xout
outfun
DIF
...
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
##---- 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, est = hd, alpha = 0.05,
plotit = TRUE, pts = NA, sm = FALSE, xout = FALSE, outfun = out,
DIF = FALSE, ...)
{
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(x1) != length(x2))
stop("x1 and y2 have different lengths")
if (length(x2) != length(y2))
stop("x2 and y2 have different lengths")
if (length(y1) != length(y2))
stop("y1 and y2 have different lengths")
xy = elimna(cbind(x1, y1, x2, y2))
x1 = xy[, 1]
y1 = xy[, 2]
x2 = xy[, 3]
y2 = xy[, 4]
if (is.na(pts[1])) {
npt <- 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, 7)
dimnames(mat) <- list(NULL, c("X", "n", "DIF", "ci.low",
"ci.hi", "p.value", "p.crit"))
for (i in 1:5) {
t1 = near(x1, x1[isub[i]], fr1)
t2 = near(x2, x1[isub[i]], fr2)
pick = as.logical(t1 * t2)
test = rmmcppb(y1[pick], y2[pick], est = est, dif = DIF,
plotit = FALSE, alpha = alpha, pr = FALSE, SEED = FALSE,
...)
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(y1[pick])
mat[i, 3] <- test$output[, 2]
mat[i, 3] <- test$output[, 2]
mat[i, 4] <- test$output[, 5]
mat[i, 5] <- test$output[, 6]
mat[i, 6] <- test$output[, 3]
}
temp2 <- order(0 - mat[, 6])
bot = c(1:nrow(mat))
dvec = sort(alpha/bot, decreasing = TRUE)
mat[temp2, 7] = dvec
}
if (!is.na(pts[1])) {
n1 <- 1
n2 <- 1
vecn <- 1
for (i in 1:length(pts)) {
n1[i] <- length(y1[near(x1, pts[i], fr1)])
n2[i] <- length(y2[near(x2, pts[i], fr2)])
}
flage = rep(TRUE, length(pts))
for (i in 1:length(pts)) {
t1 <- near(x1, pts[i], fr1)
t2 <- near(x2, pts[i], fr2)
pick = as.logical(t1 * t2)
if (sum(pick) <= 5) {
print(paste("Warning: there are", sum(pick),
" points corresponding to the design point X=",
pts[i]))
flage[i] = FALSE
}
}
pts = pts[flage]
mat <- matrix(NA, length(pts), 7)
dimnames(mat) <- list(NULL, c("X", "n", "DIF", "ci.low",
"ci.hi", "p.value", "p.crit"))
for (i in 1:length(pts)) {
t1 <- near(x1, pts[i], fr1)
t2 <- near(x2, pts[i], fr2)
pick = as.logical(t1 * t2)
test = rmmcppb(y1[pick], y2[pick], est = est, dif = DIF,
plotit = FALSE, alpha = alpha, pr = FALSE, SEED = FALSE,
...)
mat[i, 3] <- test$output[, 2]
mat[i, 1] <- pts[i]
mat[i, 2] <- length(y1[pick])
mat[i, 4] <- test$output[, 5]
mat[i, 5] <- test$output[, 6]
mat[i, 6] <- test$output[, 3]
}
temp2 <- order(0 - mat[, 6])
bot = c(1:nrow(mat))
dvec = sort(alpha/bot, decreasing = TRUE)
mat[temp2, 7] = dvec
}
if (plotit) {
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
xout = xout, outfun = outfun, , ...)
}
list(output = mat)
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Usage
1 |
Arguments
x1 |
|
y1 |
|
x2 |
|
y2 |
|
fr1 |
|
fr2 |
|
est |
|
alpha |
|
plotit |
|
pts |
|
sm |
|
xout |
|
outfun |
|
DIF |
|
... |
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 | ##---- 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, est = hd, alpha = 0.05,
plotit = TRUE, pts = NA, sm = FALSE, xout = FALSE, outfun = out,
DIF = FALSE, ...)
{
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(x1) != length(x2))
stop("x1 and y2 have different lengths")
if (length(x2) != length(y2))
stop("x2 and y2 have different lengths")
if (length(y1) != length(y2))
stop("y1 and y2 have different lengths")
xy = elimna(cbind(x1, y1, x2, y2))
x1 = xy[, 1]
y1 = xy[, 2]
x2 = xy[, 3]
y2 = xy[, 4]
if (is.na(pts[1])) {
npt <- 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, 7)
dimnames(mat) <- list(NULL, c("X", "n", "DIF", "ci.low",
"ci.hi", "p.value", "p.crit"))
for (i in 1:5) {
t1 = near(x1, x1[isub[i]], fr1)
t2 = near(x2, x1[isub[i]], fr2)
pick = as.logical(t1 * t2)
test = rmmcppb(y1[pick], y2[pick], est = est, dif = DIF,
plotit = FALSE, alpha = alpha, pr = FALSE, SEED = FALSE,
...)
mat[i, 1] <- x1[isub[i]]
mat[i, 2] <- length(y1[pick])
mat[i, 3] <- test$output[, 2]
mat[i, 3] <- test$output[, 2]
mat[i, 4] <- test$output[, 5]
mat[i, 5] <- test$output[, 6]
mat[i, 6] <- test$output[, 3]
}
temp2 <- order(0 - mat[, 6])
bot = c(1:nrow(mat))
dvec = sort(alpha/bot, decreasing = TRUE)
mat[temp2, 7] = dvec
}
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)])
}
flage = rep(TRUE, length(pts))
for (i in 1:length(pts)) {
t1 <- near(x1, pts[i], fr1)
t2 <- near(x2, pts[i], fr2)
pick = as.logical(t1 * t2)
if (sum(pick) <= 5) {
print(paste("Warning: there are", sum(pick),
" points corresponding to the design point X=",
pts[i]))
flage[i] = FALSE
}
}
pts = pts[flage]
mat <- matrix(NA, length(pts), 7)
dimnames(mat) <- list(NULL, c("X", "n", "DIF", "ci.low",
"ci.hi", "p.value", "p.crit"))
for (i in 1:length(pts)) {
t1 <- near(x1, pts[i], fr1)
t2 <- near(x2, pts[i], fr2)
pick = as.logical(t1 * t2)
test = rmmcppb(y1[pick], y2[pick], est = est, dif = DIF,
plotit = FALSE, alpha = alpha, pr = FALSE, SEED = FALSE,
...)
mat[i, 3] <- test$output[, 2]
mat[i, 1] <- pts[i]
mat[i, 2] <- length(y1[pick])
mat[i, 4] <- test$output[, 5]
mat[i, 5] <- test$output[, 6]
mat[i, 6] <- test$output[, 3]
}
temp2 <- order(0 - mat[, 6])
bot = c(1:nrow(mat))
dvec = sort(alpha/bot, decreasing = TRUE)
mat[temp2, 7] = dvec
}
if (plotit) {
runmean2g(x1, y1, x2, y2, fr = fr1, est = est, sm = sm,
xout = xout, outfun = outfun, , ...)
}
list(output = mat)
}
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.