| Wilcoxon {stats} | R Documentation |
Density, distribution function, quantile function and random
generation for the distribution of the Wilcoxon rank sum statistic
obtained from samples with size m and n, respectively.
dwilcox(x, m, n, log = FALSE) pwilcox(q, m, n, lower.tail = TRUE, log.p = FALSE) qwilcox(p, m, n, lower.tail = TRUE, log.p = FALSE) rwilcox(nn, m, n)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
nn |
number of observations. If |
m, n |
numbers of observations in the first and second sample, respectively. Can be vectors of positive integers. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x]. |
This distribution is obtained as follows. Let x and y
be two random, independent samples of size m and n.
Then the Wilcoxon rank sum statistic is the number of all pairs
(x[i], y[j]) for which y[j] is not greater than
x[i]. This statistic takes values between 0 and
m * n, and its mean and variance are m * n / 2 and
m * n * (m + n + 1) / 12, respectively.
If any of the first three arguments are vectors, the recycling rule is used to do the calculations for all combinations of the three up to the length of the longest vector.
dwilcox gives the density,
pwilcox gives the distribution function,
qwilcox gives the quantile function, and
rwilcox generates random deviates.
The length of the result is determined by nn for
rwilcox, and is the maximum of the lengths of the
numerical arguments for the other functions.
The numerical arguments other than nn are recycled to the
length of the result. Only the first elements of the logical
arguments are used.
These functions can use large amounts of memory and stack (and even crash R if the stack limit is exceeded and stack-checking is not in place) if one sample is large (several thousands or more).
S-PLUS uses a different (but equivalent) definition of the Wilcoxon
statistic: see wilcox.test for details.
Kurt Hornik
These are calculated via recursion, based on cwilcox(k, m, n),
the number of choices with statistic k from samples of size
m and n, which is itself calculated recursively and the
results cached. Then dwilcox and pwilcox sum
appropriate values of cwilcox, and qwilcox is based on
inversion.
rwilcox generates a random permutation of ranks and evaluates
the statistic.
wilcox.test to calculate the statistic from data, find p
values and so on.
Distributions for standard distributions, including
dsignrank for the distribution of the
one-sample Wilcoxon signed rank statistic.
require(graphics)
x <- -1:(4*6 + 1)
fx <- dwilcox(x, 4, 6)
Fx <- pwilcox(x, 4, 6)
layout(rbind(1,2), widths = 1, heights = c(3,2))
plot(x, fx, type = "h", col = "violet",
main = "Probabilities (density) of Wilcoxon-Statist.(n=6, m=4)")
plot(x, Fx, type = "s", col = "blue",
main = "Distribution of Wilcoxon-Statist.(n=6, m=4)")
abline(h = 0:1, col = "gray20", lty = 2)
layout(1) # set back
N <- 200
hist(U <- rwilcox(N, m = 4,n = 6), breaks = 0:25 - 1/2,
border = "red", col = "pink", sub = paste("N =",N))
mtext("N * f(x), f() = true \"density\"", side = 3, col = "blue")
lines(x, N*fx, type = "h", col = "blue", lwd = 2)
points(x, N*fx, cex = 2)
## Better is a Quantile-Quantile Plot
qqplot(U, qw <- qwilcox((1:N - 1/2)/N, m = 4, n = 6),
main = paste("Q-Q-Plot of empirical and theoretical quantiles",
"Wilcoxon Statistic, (m=4, n=6)", sep = "\n"))
n <- as.numeric(names(print(tU <- table(U))))
text(n+.2, n+.5, labels = tU, col = "red")