wilcox.test              package:ctest              R Documentation

_W_i_l_c_o_x_o_n _R_a_n_k _S_u_m _a_n_d _S_i_g_n_e_d _R_a_n_k _T_e_s_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Performs one and two sample Wilcoxon tests on vectors of data.

_U_s_a_g_e:

     wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),
                 mu = 0, paired = FALSE, exact = NULL, correct = TRUE, 
                 conf.int = FALSE, conf.level = 0.95)

_A_r_g_u_m_e_n_t_s:

       x: numeric vector of data values.

       y: an optional numeric vector of data values.

alternative: the alternative hypothesis must be one of `"two.sided"'
          (default), `"greater"' or `"less"'.  You can specify just the
          initial letter.

      mu: a number specifying an optional location parameter.

  paired: a logical indicating whether you want a paired test.

   exact: a logical indicating whether an exact p-value should be
          computed.

 correct: a logical indicating whether to apply continuity correction
          in the normal approximation for the p-value.

conf.int: a logical indicating whether a confidence interval should be
          computed.

conf.level: confidence level of the interval.

_D_e_t_a_i_l_s:

     If only `x' is given, or if both `x' and `y' are given and
     `paired' is `TRUE', a Wilcoxon signed rank test of the null that
     the median of `x' (in the one sample case) or of `x-y' (in the
     paired two sample case) equals `mu' is performed.

     Otherwise, if both `x' and `y' are given and `paired' is `FALSE',
     a Wilcoxon rank sum test (equivalent to the Mann-Whitney test) is
     carried out.  In this case, the null hypothesis is that the
     location of the distributions of `x' and `y' differ by `mu'.

     By default (if `exact' is not specified), an exact p-value is
     computed if the samples contain less than 50 finite values and
     there are no ties.  Otherwise, a normal approximation is used.

     Optionally (if argument `conf.int' is true), a nonparametric
     confidence interval for the median (one-sample case) or for the
     difference of the location parameters `x-y' is computed.  If exact
     p-values are available, an exact confidence interval is obtained
     by the algorithm described in Bauer (1972).  Otherwise, an
     asymptotic confidence interval is returned.

_V_a_l_u_e:

     A list with class `"htest"' containing the following components: 

statistic: the value of the test statistic with a name describing it.

parameter: the parameter(s) for the exact distribution of the test
          statistic.

 p.value: the p-value for the test.

null.value: the location parameter `mu'.

alternative: a character string describing the alternative hypothesis.

  method: the type of test applied.

data.name: a character string giving the names of the data.

conf.int: a confidence interval for the location parameter. (Only
          present if argument `conf.int = TRUE'.)

_R_e_f_e_r_e_n_c_e_s:

     Myles Hollander & Douglas A. Wolfe (1973), Nonparametric
     statistical inference. New York: John Wiley & Sons. Pages 27-33
     (one-sample), 68-75 (two-sample).

     David F. Bauer (1972), Constructing confidence sets using rank
     statistics. Journal of the American Statistical Association 67,
     687-690.

_S_e_e _A_l_s_o:

     `kruskal.test' for testing homogeneity in location parameters in
     the case of two or more samples; `t.test' for a parametric
     alternative under normality assumptions.

_E_x_a_m_p_l_e_s:

     ## One-sample test.
     ## Hollander & Wolfe (1973), 29f.
     ## Hamilton depression scale factor measurements in 9 patients with
     ##  mixed anxiety and depression, taken at the first (x) and second
     ##  (y) visit after initiation of a therapy (administration of a
     ##  tranquilizer).
     x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
     y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
     wilcox.test(x, y, paired = TRUE, alternative = "greater")
     wilcox.test(y - x, alternative = "less")    # The same.
     wilcox.test(y - x, alternative = "less",
                 exact = FALSE, correct = FALSE) # H&W large sample
                                                 # approximation

     ## Two-sample test.
     ## Hollander & Wolfe (1973), 69f.
     ## Permeability constants of the human chorioamnion (a placental
     ##  membrane) at term (x) and between 12 to 26 weeks gestational
     ##  age (y).  The alternative of interest is greater permeability
     ##  of the human chorioamnion for the term pregnancy.
     x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
     y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
     wilcox.test(x, y, alternative = "g")        # greater
     wilcox.test(x, y, alternative = "greater",
                 exact = FALSE, correct = FALSE) # H&W large sample
                                                 # approximation

     wilcox.test(rnorm(10), rnorm(10, 2), conf.int = TRUE)

