Wilcoxon {stats} R Documentation

## Distribution of the Wilcoxon Rank Sum Statistic

### Description

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.

### 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 <= 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.

### Note

S-PLUS uses a different (but equivalent) definition of the Wilcoxon statistic.

### Author(s)

Kurt Hornik

`wilcox.test` to calculate the statistic from data, find p values and so on.

`dsignrank` etc, for the distribution of the one-sample Wilcoxon signed rank statistic.

### Examples

```x <- -1:(4*6 + 1)
fx <- dwilcox(x, 4, 6)
Fx <- pwilcox(x, 4, 6)

layout(rbind(1,2),width=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")
```

[Package stats version 2.1.0 Index]