# Wilcoxon

##### Distribution of the Wilcoxon Rank Sum Statistic

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.

- Keywords
- distribution

##### Usage

```
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)
```

##### Arguments

- x, q
vector of quantiles.

- p
vector of probabilities.

- nn
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).

- lower.tail
logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X > x]\).

##### Details

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.

##### Value

`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.

##### Note

S-PLUS uses a different (but equivalent) definition of the Wilcoxon
statistic: see `wilcox.test`

for details.

##### Warning

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).

##### See Also

`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.

##### Examples

`library(stats)`

```
# NOT RUN {
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 package stats, version 3.6.1, License: Part of R 3.6.1*