ancovaV2:
In musto101/wilcox_R: What the package does (short line)
Usage
1
ancovaV2(x1 = NULL, y1 = NULL, x2 = NULL, y2 = NULL, fr1 = 1, fr2 = 1, p.crit = NULL, padj = FALSE, pr = TRUE, method = "hochberg", FAST = TRUE, est = tmean, alpha = 0.05, plotit = TRUE, xlab = "X", ylab = "Y", qpts = FALSE, qvals = c(0.25, 0.5, 0.75), sm = FALSE, xout = FALSE, eout = FALSE, outfun = out, LP = TRUE, nboot = 500, SEED = TRUE, nreps = 2000, MC = FALSE, nmin = 12, q = 0.5, SCAT = TRUE, pch1 = "*", pch2 = "+", ...)
Arguments
x1
y1
x2
y2
fr1
fr2
p.crit
padj
pr
method
FAST
est
alpha
plotit
xlab
ylab
qpts
qvals
sm
xout
eout
outfun
LP
nboot
SEED
nreps
MC
nmin
q
SCAT
pch1
pch2
...
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 = NULL, y1 = NULL, x2 = NULL, y2 = NULL, fr1 = 1,
fr2 = 1, p.crit = NULL, padj = FALSE, pr = TRUE, method = "hochberg",
FAST = TRUE, est = tmean, alpha = 0.05, plotit = TRUE, xlab = "X",
ylab = "Y", qpts = FALSE, qvals = c(0.25, 0.5, 0.75), sm = FALSE,
xout = FALSE, eout = FALSE, outfun = out, LP = TRUE, nboot = 500,
SEED = TRUE, nreps = 2000, MC = FALSE, nmin = 12, q = 0.5,
SCAT = TRUE, pch1 = "*", pch2 = "+", ...)
{
if (SEED)
set.seed(2)
if (pr) {
if (!FAST) {
if (!MC & is.null(p.crit))
print("For faster execution time, set MC=TRUE, assuming a multi-core processor is available")
}
}
if (padj)
p.crit = 0
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")
xy1 = elimna(cbind(x1, y1))
xy2 = elimna = cbind(x2, y2)
n1 = nrow(xy1)
n2 = nrow(xy2)
if (plotit) {
ef = identical(est, hd)
if (!ef)
runmean2g(xy1[, 1], xy1[, 2], xy2[, 1], xy2[, 2],
fr = fr1, est = est, sm = sm, xout = xout, LP = LP,
eout = eout, xlab = xlab, ylab = ylab, SCAT = SCAT,
pch1 = pch1, pch2 = pch2, ...)
if (ef)
runmean2g(xy1[, 1], xy1[, 2], xy2[, 1], xy2[, 2],
fr = fr1, est = hd, sm = sm, xout = xout, LP = LP,
q = q, eout = eout, xlab = xlab, ylab = ylab,
SCAT = SCAT, pch1 = pch1, pch2 = pch2, ...)
}
if (is.null(p.crit)) {
if (FAST) {
if (alpha == 0.05) {
nm = max(c(n1, n2))
if (nm <= 800) {
nv = c(50, 60, 80, 100, 200, 300, 500, 800)
if (qpts) {
pv = c(0.02709, 0.0283, 0.0306, 0.02842,
0.02779, 0.0241, 0.02683, 0.01868, 0.02122)
p.crit = lplot.pred(1/nv, pv, 1/n1)$yhat
}
if (!qpts) {
pv = c(0.020831, 0.017812, 0.015796, 0.014773,
0.012589, 0.015664, 0.011803, 0.012479)
p.crit = lplot.pred(1/nv, pv, 1/n1)$yhat
}
}
}
}
}
if (is.null(p.crit)) {
p.crit = ancovaV2.pv(n1, n2, nreps = nreps, MC = MC,
qpts = qpts, est = est, qvals = qvals, SEED = SEED,
alpha = alpha, nboot = nboot)$p.crit
}
pts = NULL
if (qpts)
for (i in 1:length(qvals)) pts = c(pts, qest(xy1[, 1],
qvals[i]))
if (!qpts)
pts = ancova(x1, y1, x2, y2, pr = FALSE, plotit = FALSE)$output[,
1]
if (SEED)
set.seed(2)
ef = identical(est, hd)
est1 = NA
est2 = NA
J = length(pts)
est1 = matrix(NA, nrow = nboot, ncol = J)
est2 = matrix(NA, nrow = nboot, ncol = J)
data1 = matrix(sample(n1, size = n1 * nboot, replace = TRUE),
ncol = nboot, nrow = n1)
data2 = matrix(sample(n2, size = n2 * nboot, replace = TRUE),
ncol = nboot, nrow = n2)
if (!MC) {
if (!ef) {
est1 = apply(data1, 2, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = est, fr = fr1, nmin = nmin,
...)
est2 = apply(data2, 2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = est, fr = fr2, nmin = nmin,
...)
}
if (ef) {
est1 = apply(data1, 2, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = hd, fr = fr1, nmin = nmin, q = q,
...)
est2 = apply(data2, 2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = hd, fr = fr2, nmin = nmin, q = q,
...)
}
est1 = t(as.matrix(est1))
est2 = t(as.matrix(est2))
}
if (MC) {
library(parallel)
data1 = listm(data1)
data2 = listm(data2)
if (!ef) {
est1 = mclapply(data1, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = est, fr = fr1, nmin = nmin,
...)
est2 = mclapply(data2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = est, fr = fr2, nmin = nmin,
...)
}
if (ef) {
est1 = mclapply(data1, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = hd, fr = fr1, nmin = nmin, q = q,
...)
est2 = mclapply(data2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = hd, fr = fr2, nmin = nmin, q = q,
...)
}
est1 = t(matl(est1))
est2 = t(matl(est2))
}
pv = NULL
if (J == 1) {
est1 = t(as.matrix(est1))
est2 = t(as.matrix(est2))
}
for (j in 1:J) {
pv[j] = mean(est1[, j] < est2[, j], na.rm = TRUE) + 0.5 *
mean(est1[, j] == est2[, j], na.rm = TRUE)
pv[j] = 2 * min(c(pv[j], 1 - pv[j]))
}
pvadj = rep(NA, length(pts))
if (padj)
pvadj = p.adjust(pv, method = method)
pvm = cbind(pts, pv, pvadj)
dimnames(pvm) = list(NULL, c("X", "p.values", "p.adjusted"))
list(output = pvm, n = c(n1, n2), p.crit = p.crit)
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
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Usage
1 | ancovaV2(x1 = NULL, y1 = NULL, x2 = NULL, y2 = NULL, fr1 = 1, fr2 = 1, p.crit = NULL, padj = FALSE, pr = TRUE, method = "hochberg", FAST = TRUE, est = tmean, alpha = 0.05, plotit = TRUE, xlab = "X", ylab = "Y", qpts = FALSE, qvals = c(0.25, 0.5, 0.75), sm = FALSE, xout = FALSE, eout = FALSE, outfun = out, LP = TRUE, nboot = 500, SEED = TRUE, nreps = 2000, MC = FALSE, nmin = 12, q = 0.5, SCAT = TRUE, pch1 = "*", pch2 = "+", ...)
|
Arguments
x1 |
|
y1 |
|
x2 |
|
y2 |
|
fr1 |
|
fr2 |
|
p.crit |
|
padj |
|
pr |
|
method |
|
FAST |
|
est |
|
alpha |
|
plotit |
|
xlab |
|
ylab |
|
qpts |
|
qvals |
|
sm |
|
xout |
|
eout |
|
outfun |
|
LP |
|
nboot |
|
SEED |
|
nreps |
|
MC |
|
nmin |
|
q |
|
SCAT |
|
pch1 |
|
pch2 |
|
... |
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 | ##---- 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 = NULL, y1 = NULL, x2 = NULL, y2 = NULL, fr1 = 1,
fr2 = 1, p.crit = NULL, padj = FALSE, pr = TRUE, method = "hochberg",
FAST = TRUE, est = tmean, alpha = 0.05, plotit = TRUE, xlab = "X",
ylab = "Y", qpts = FALSE, qvals = c(0.25, 0.5, 0.75), sm = FALSE,
xout = FALSE, eout = FALSE, outfun = out, LP = TRUE, nboot = 500,
SEED = TRUE, nreps = 2000, MC = FALSE, nmin = 12, q = 0.5,
SCAT = TRUE, pch1 = "*", pch2 = "+", ...)
{
if (SEED)
set.seed(2)
if (pr) {
if (!FAST) {
if (!MC & is.null(p.crit))
print("For faster execution time, set MC=TRUE, assuming a multi-core processor is available")
}
}
if (padj)
p.crit = 0
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")
xy1 = elimna(cbind(x1, y1))
xy2 = elimna = cbind(x2, y2)
n1 = nrow(xy1)
n2 = nrow(xy2)
if (plotit) {
ef = identical(est, hd)
if (!ef)
runmean2g(xy1[, 1], xy1[, 2], xy2[, 1], xy2[, 2],
fr = fr1, est = est, sm = sm, xout = xout, LP = LP,
eout = eout, xlab = xlab, ylab = ylab, SCAT = SCAT,
pch1 = pch1, pch2 = pch2, ...)
if (ef)
runmean2g(xy1[, 1], xy1[, 2], xy2[, 1], xy2[, 2],
fr = fr1, est = hd, sm = sm, xout = xout, LP = LP,
q = q, eout = eout, xlab = xlab, ylab = ylab,
SCAT = SCAT, pch1 = pch1, pch2 = pch2, ...)
}
if (is.null(p.crit)) {
if (FAST) {
if (alpha == 0.05) {
nm = max(c(n1, n2))
if (nm <= 800) {
nv = c(50, 60, 80, 100, 200, 300, 500, 800)
if (qpts) {
pv = c(0.02709, 0.0283, 0.0306, 0.02842,
0.02779, 0.0241, 0.02683, 0.01868, 0.02122)
p.crit = lplot.pred(1/nv, pv, 1/n1)$yhat
}
if (!qpts) {
pv = c(0.020831, 0.017812, 0.015796, 0.014773,
0.012589, 0.015664, 0.011803, 0.012479)
p.crit = lplot.pred(1/nv, pv, 1/n1)$yhat
}
}
}
}
}
if (is.null(p.crit)) {
p.crit = ancovaV2.pv(n1, n2, nreps = nreps, MC = MC,
qpts = qpts, est = est, qvals = qvals, SEED = SEED,
alpha = alpha, nboot = nboot)$p.crit
}
pts = NULL
if (qpts)
for (i in 1:length(qvals)) pts = c(pts, qest(xy1[, 1],
qvals[i]))
if (!qpts)
pts = ancova(x1, y1, x2, y2, pr = FALSE, plotit = FALSE)$output[,
1]
if (SEED)
set.seed(2)
ef = identical(est, hd)
est1 = NA
est2 = NA
J = length(pts)
est1 = matrix(NA, nrow = nboot, ncol = J)
est2 = matrix(NA, nrow = nboot, ncol = J)
data1 = matrix(sample(n1, size = n1 * nboot, replace = TRUE),
ncol = nboot, nrow = n1)
data2 = matrix(sample(n2, size = n2 * nboot, replace = TRUE),
ncol = nboot, nrow = n2)
if (!MC) {
if (!ef) {
est1 = apply(data1, 2, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = est, fr = fr1, nmin = nmin,
...)
est2 = apply(data2, 2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = est, fr = fr2, nmin = nmin,
...)
}
if (ef) {
est1 = apply(data1, 2, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = hd, fr = fr1, nmin = nmin, q = q,
...)
est2 = apply(data2, 2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = hd, fr = fr2, nmin = nmin, q = q,
...)
}
est1 = t(as.matrix(est1))
est2 = t(as.matrix(est2))
}
if (MC) {
library(parallel)
data1 = listm(data1)
data2 = listm(data2)
if (!ef) {
est1 = mclapply(data1, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = est, fr = fr1, nmin = nmin,
...)
est2 = mclapply(data2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = est, fr = fr2, nmin = nmin,
...)
}
if (ef) {
est1 = mclapply(data1, DancGLOB_sub, xy = xy1[, 1:2],
pts = pts, est = hd, fr = fr1, nmin = nmin, q = q,
...)
est2 = mclapply(data2, DancGLOB_sub, xy = xy2[, 1:2],
pts = pts, est = hd, fr = fr2, nmin = nmin, q = q,
...)
}
est1 = t(matl(est1))
est2 = t(matl(est2))
}
pv = NULL
if (J == 1) {
est1 = t(as.matrix(est1))
est2 = t(as.matrix(est2))
}
for (j in 1:J) {
pv[j] = mean(est1[, j] < est2[, j], na.rm = TRUE) + 0.5 *
mean(est1[, j] == est2[, j], na.rm = TRUE)
pv[j] = 2 * min(c(pv[j], 1 - pv[j]))
}
pvadj = rep(NA, length(pts))
if (padj)
pvadj = p.adjust(pv, method = method)
pvm = cbind(pts, pv, pvadj)
dimnames(pvm) = list(NULL, c("X", "p.values", "p.adjusted"))
list(output = pvm, n = c(n1, n2), p.crit = p.crit)
}
|
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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