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 `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]. |

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.

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.

`dsignrank`

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

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 2.5.0 Index]