Wilcoxon                package:base                R Documentation

_D_i_s_t_r_i_b_u_t_i_o_n _o_f _t_h_e _W_i_l_c_o_x_o_n _R_a_n_k _S_u_m _S_t_a_t_i_s_t_i_c

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     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)

_A_r_g_u_m_e_n_t_s:

    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.

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].

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     `dwilcox' gives the density, `pwilcox' gives the distribution
     function, `qwilcox' gives the quantile function, and `rwilcox'
     generates random deviates.

_A_u_t_h_o_r(_s):

     Kurt Hornik hornik@ci.tuwien.ac.at

_S_e_e _A_l_s_o:

     `dsignrank' etc, for the one-sample Wilcoxon rank statistic.

_E_x_a_m_p_l_e_s:

     x <- -1:(4*6 + 1)
     fx <- dwilcox(x, 4, 6)
     all(fx == dwilcox(x, 6, 4))
     Fx <- pwilcox(x, 4, 6)
     all(abs(Fx - cumsum(fx)) < 10 * .Machine$double.eps)

     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")

