Density, distribution function, quantile function and random generation for the distribution of the Wilcoxon rank sum statistic obtained from samples with size
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.
- vector of probabilities.
- number of observations. If
length(nn) > 1, the length is taken to be the number required.
- 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).
- logical; if TRUE (default), probabilities are P[X ≤ x], otherwise, P[X > x].
This distribution is obtained as follows. Let
y be two random, independent samples of size
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
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.
The length of the result is determined by
rwilcox, and is the maximum of the lengths of the numerical parameters for the other functions. The numerical parameters other than
nn are recycled to the length of the result. Only the first elements of the logical parameters 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.
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")
Documentation reproduced from R 2.15.3. License: GPL-2.