dmedpb:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
x
y
alpha
con
est
plotit
dif
grp
hoch
nboot
xlab
ylab
pr
SEED
BA
...
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 (x, y = NULL, alpha = 0.05, con = 0, est = median, plotit = TRUE,
dif = TRUE, grp = NA, hoch = TRUE, nboot = NA, xlab = "Group 1",
ylab = "Group 2", pr = TRUE, SEED = TRUE, BA = FALSE, ...)
{
if (dif) {
if (pr)
print("dif=T, so analysis is done on difference scores")
temp <- rmmcppbd(x, y = y, alpha = alpha, con = con,
est = est, plotit = plotit, grp = grp, nboot = nboot,
hoch = hoch, ...)
output <- temp$output
con <- temp$con
}
if (!dif) {
if (pr)
print("dif=F, so analysis is done on marginal distributions")
if (!is.null(y[1]))
x <- cbind(x, y)
if (is.data.frame(x))
x = as.matrix(x)
if (!is.list(x) && !is.matrix(x))
stop("Data must be stored in a matrix or in list mode.")
if (is.list(x)) {
if (is.matrix(con)) {
if (length(x) != nrow(con))
stop("The number of rows in con is not equal to the number of groups.")
}
}
if (is.list(x)) {
mat <- matl(x)
}
if (is.matrix(x) && is.matrix(con)) {
if (ncol(x) != nrow(con))
stop("The number of rows in con is not equal to the number of groups.")
mat <- x
}
if (is.matrix(x))
mat <- x
if (!is.na(sum(grp)))
mat <- mat[, grp]
mat <- elimna(mat)
x <- mat
J <- ncol(mat)
xcen <- x
for (j in 1:J) xcen[, j] <- x[, j] - est(x[, j])
Jm <- J - 1
if (sum(con^2) == 0) {
d <- (J^2 - J)/2
con <- matrix(0, J, d)
id <- 0
for (j in 1:Jm) {
jp <- j + 1
for (k in jp:J) {
id <- id + 1
con[j, id] <- 1
con[k, id] <- 0 - 1
}
}
}
d <- ncol(con)
if (is.na(nboot)) {
if (d <= 4)
nboot <- 1000
if (d > 4)
nboot <- 5000
}
n <- nrow(mat)
crit.vec <- alpha/c(1:d)
connum <- ncol(con)
if (SEED)
set.seed(2)
xbars <- apply(mat, 2, est)
psidat <- NA
for (ic in 1:connum) psidat[ic] <- sum(con[, ic] * xbars)
psihat <- matrix(0, connum, nboot)
psihatcen <- matrix(0, connum, nboot)
bvec <- matrix(NA, ncol = J, nrow = nboot)
bveccen <- matrix(NA, ncol = J, nrow = nboot)
print("Taking bootstrap samples. Please wait.")
data <- matrix(sample(n, size = n * nboot, replace = TRUE),
nrow = nboot)
for (ib in 1:nboot) {
bvec[ib, ] <- apply(x[data[ib, ], ], 2, est, ...)
bveccen[ib, ] <- apply(xcen[data[ib, ], ], 2, est,
...)
}
test <- 1
bias <- NA
tval <- NA
tvalcen <- NA
for (ic in 1:connum) {
psihat[ic, ] <- apply(bvec, 1, bptdpsi, con[, ic])
psihatcen[ic, ] <- apply(bveccen, 1, bptdpsi, con[,
ic])
tvalcen[ic] <- sum((psihatcen[ic, ] == 0))/nboot
bias[ic] <- sum((psihatcen[ic, ] > 0))/nboot + sum((psihatcen[ic,
] == 0))/nboot - 0.5
tval[ic] <- sum((psihat[ic, ] == 0))/nboot
if (BA) {
test[ic] <- sum((psihat[ic, ] > 0))/nboot + tval[ic] -
0.1 * bias[ic]
if (test[ic] < 0)
test[ic] <- 0
}
if (!BA)
test[ic] <- sum((psihat[ic, ] > 0))/nboot + 0.5 *
tval[ic]
test[ic] <- min(test[ic], 1 - test[ic])
}
test <- 2 * test
ncon <- ncol(con)
dvec <- alpha/c(1:ncon)
if (alpha == 0.05) {
dvec <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102, 0.00851,
0.0073, 0.00639, 0.00568, 0.00511)
dvecba <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102,
0.00851, 0.0073, 0.00639, 0.00568, 0.00511)
if (ncon > 10) {
avec <- 0.05/c(11:ncon)
dvec <- c(dvec, avec)
}
}
if (alpha == 0.01) {
dvec <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201,
0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
dvecba <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201,
0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
if (ncon > 10) {
avec <- 0.01/c(11:ncon)
dvec <- c(dvec, avec)
}
}
if (hoch)
dvec <- alpha/(2 * c(1:ncon))
dvec <- 2 * dvec
if (alpha != 0.05 && alpha != 0.01) {
dvec <- alpha/c(1:ncon)
dvecba <- dvec
}
if (plotit && ncol(bvec) == 2) {
z <- c(0, 0)
one <- c(1, 1)
plot(rbind(bvec, z, one), xlab = xlab, ylab = ylab,
type = "n")
points(bvec)
totv <- apply(x, 2, est, ...)
cmat <- var(bvec)
dis <- mahalanobis(bvec, totv, cmat)
temp.dis <- order(dis)
ic <- round((1 - alpha) * nboot)
xx <- bvec[temp.dis[1:ic], ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ])
lines(xx[c(temp[1], temp[length(temp)]), ])
abline(0, 1)
}
temp2 <- order(0 - test)
ncon <- ncol(con)
zvec <- dvec[1:ncon]
if (BA)
zvec <- dvecba[1:ncon]
sigvec <- (test[temp2] >= zvec)
output <- matrix(0, connum, 6)
dimnames(output) <- list(NULL, c("con.num", "psihat",
"p-value", "p.crit", "ci.lower", "ci.upper"))
tmeans <- apply(mat, 2, est, ...)
psi <- 1
for (ic in 1:ncol(con)) {
output[ic, 2] <- sum(con[, ic] * tmeans)
output[ic, 1] <- ic
output[ic, 3] <- test[ic]
output[temp2, 4] <- zvec
temp <- sort(psihat[ic, ])
icl <- round(output[ic, 4] * nboot/2) + 1
icu <- nboot - (icl - 1)
output[ic, 5] <- temp[icl]
output[ic, 6] <- temp[icu]
}
}
num.sig <- sum(output[, 3] <= output[, 4])
list(output = output, con = con, num.sig = num.sig)
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Usage
1 |
Arguments
x |
|
y |
|
alpha |
|
con |
|
est |
|
plotit |
|
dif |
|
grp |
|
hoch |
|
nboot |
|
xlab |
|
ylab |
|
pr |
|
SEED |
|
BA |
|
... |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | ##---- 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 (x, y = NULL, alpha = 0.05, con = 0, est = median, plotit = TRUE,
dif = TRUE, grp = NA, hoch = TRUE, nboot = NA, xlab = "Group 1",
ylab = "Group 2", pr = TRUE, SEED = TRUE, BA = FALSE, ...)
{
if (dif) {
if (pr)
print("dif=T, so analysis is done on difference scores")
temp <- rmmcppbd(x, y = y, alpha = alpha, con = con,
est = est, plotit = plotit, grp = grp, nboot = nboot,
hoch = hoch, ...)
output <- temp$output
con <- temp$con
}
if (!dif) {
if (pr)
print("dif=F, so analysis is done on marginal distributions")
if (!is.null(y[1]))
x <- cbind(x, y)
if (is.data.frame(x))
x = as.matrix(x)
if (!is.list(x) && !is.matrix(x))
stop("Data must be stored in a matrix or in list mode.")
if (is.list(x)) {
if (is.matrix(con)) {
if (length(x) != nrow(con))
stop("The number of rows in con is not equal to the number of groups.")
}
}
if (is.list(x)) {
mat <- matl(x)
}
if (is.matrix(x) && is.matrix(con)) {
if (ncol(x) != nrow(con))
stop("The number of rows in con is not equal to the number of groups.")
mat <- x
}
if (is.matrix(x))
mat <- x
if (!is.na(sum(grp)))
mat <- mat[, grp]
mat <- elimna(mat)
x <- mat
J <- ncol(mat)
xcen <- x
for (j in 1:J) xcen[, j] <- x[, j] - est(x[, j])
Jm <- J - 1
if (sum(con^2) == 0) {
d <- (J^2 - J)/2
con <- matrix(0, J, d)
id <- 0
for (j in 1:Jm) {
jp <- j + 1
for (k in jp:J) {
id <- id + 1
con[j, id] <- 1
con[k, id] <- 0 - 1
}
}
}
d <- ncol(con)
if (is.na(nboot)) {
if (d <= 4)
nboot <- 1000
if (d > 4)
nboot <- 5000
}
n <- nrow(mat)
crit.vec <- alpha/c(1:d)
connum <- ncol(con)
if (SEED)
set.seed(2)
xbars <- apply(mat, 2, est)
psidat <- NA
for (ic in 1:connum) psidat[ic] <- sum(con[, ic] * xbars)
psihat <- matrix(0, connum, nboot)
psihatcen <- matrix(0, connum, nboot)
bvec <- matrix(NA, ncol = J, nrow = nboot)
bveccen <- matrix(NA, ncol = J, nrow = nboot)
print("Taking bootstrap samples. Please wait.")
data <- matrix(sample(n, size = n * nboot, replace = TRUE),
nrow = nboot)
for (ib in 1:nboot) {
bvec[ib, ] <- apply(x[data[ib, ], ], 2, est, ...)
bveccen[ib, ] <- apply(xcen[data[ib, ], ], 2, est,
...)
}
test <- 1
bias <- NA
tval <- NA
tvalcen <- NA
for (ic in 1:connum) {
psihat[ic, ] <- apply(bvec, 1, bptdpsi, con[, ic])
psihatcen[ic, ] <- apply(bveccen, 1, bptdpsi, con[,
ic])
tvalcen[ic] <- sum((psihatcen[ic, ] == 0))/nboot
bias[ic] <- sum((psihatcen[ic, ] > 0))/nboot + sum((psihatcen[ic,
] == 0))/nboot - 0.5
tval[ic] <- sum((psihat[ic, ] == 0))/nboot
if (BA) {
test[ic] <- sum((psihat[ic, ] > 0))/nboot + tval[ic] -
0.1 * bias[ic]
if (test[ic] < 0)
test[ic] <- 0
}
if (!BA)
test[ic] <- sum((psihat[ic, ] > 0))/nboot + 0.5 *
tval[ic]
test[ic] <- min(test[ic], 1 - test[ic])
}
test <- 2 * test
ncon <- ncol(con)
dvec <- alpha/c(1:ncon)
if (alpha == 0.05) {
dvec <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102, 0.00851,
0.0073, 0.00639, 0.00568, 0.00511)
dvecba <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102,
0.00851, 0.0073, 0.00639, 0.00568, 0.00511)
if (ncon > 10) {
avec <- 0.05/c(11:ncon)
dvec <- c(dvec, avec)
}
}
if (alpha == 0.01) {
dvec <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201,
0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
dvecba <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201,
0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
if (ncon > 10) {
avec <- 0.01/c(11:ncon)
dvec <- c(dvec, avec)
}
}
if (hoch)
dvec <- alpha/(2 * c(1:ncon))
dvec <- 2 * dvec
if (alpha != 0.05 && alpha != 0.01) {
dvec <- alpha/c(1:ncon)
dvecba <- dvec
}
if (plotit && ncol(bvec) == 2) {
z <- c(0, 0)
one <- c(1, 1)
plot(rbind(bvec, z, one), xlab = xlab, ylab = ylab,
type = "n")
points(bvec)
totv <- apply(x, 2, est, ...)
cmat <- var(bvec)
dis <- mahalanobis(bvec, totv, cmat)
temp.dis <- order(dis)
ic <- round((1 - alpha) * nboot)
xx <- bvec[temp.dis[1:ic], ]
xord <- order(xx[, 1])
xx <- xx[xord, ]
temp <- chull(xx)
lines(xx[temp, ])
lines(xx[c(temp[1], temp[length(temp)]), ])
abline(0, 1)
}
temp2 <- order(0 - test)
ncon <- ncol(con)
zvec <- dvec[1:ncon]
if (BA)
zvec <- dvecba[1:ncon]
sigvec <- (test[temp2] >= zvec)
output <- matrix(0, connum, 6)
dimnames(output) <- list(NULL, c("con.num", "psihat",
"p-value", "p.crit", "ci.lower", "ci.upper"))
tmeans <- apply(mat, 2, est, ...)
psi <- 1
for (ic in 1:ncol(con)) {
output[ic, 2] <- sum(con[, ic] * tmeans)
output[ic, 1] <- ic
output[ic, 3] <- test[ic]
output[temp2, 4] <- zvec
temp <- sort(psihat[ic, ])
icl <- round(output[ic, 4] * nboot/2) + 1
icu <- nboot - (icl - 1)
output[ic, 5] <- temp[icl]
output[ic, 6] <- temp[icu]
}
}
num.sig <- sum(output[, 3] <= output[, 4])
list(output = output, con = con, num.sig = num.sig)
}
|
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