kruskal.test              package:ctest              R Documentation

_K_r_u_s_k_a_l-_W_a_l_l_i_s _R_a_n_k _S_u_m _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     Performs a Kruskal-Wallis rank sum test.

_U_s_a_g_e:

     kruskal.test(x, g)

_A_r_g_u_m_e_n_t_s:

       x: a numeric vector of data values, or a list of numeric data
          vectors.

       g: a vector or factor object giving the group for the
          corresponding elements of `x'.  Ignored if `x' is a list.

_D_e_t_a_i_l_s:

     `kruskal.test' performs a Kruskal-Wallis rank sum test of the null
     that the location parameters of the distribution of `x' are the
     same in each group (sample).  The alternative is that they differ
     in at least one.

     If `x' is a list, its elements are taken as the samples to be
     compared, and hence have to be numeric data vectors.  In this
     case, `g' is ignored, and one can simply use `kruskal.test(x)' to
     perform the test.  If the samples are not yet contained in a list,
     use `kruskal.test(list(x, ...))'.

     Otherwise, `x' must be a numeric data vector, and `g' must be a
     vector or factor object of the same length as `x' giving the group
     for the corresponding elements of `x'.

_V_a_l_u_e:

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

statistic: the Kruskal-Wallis rank sum statistic.

parameter: the degrees of freedom of the approximate chi-squared
          distribution of the test statistic.

 p.value: the p-value of the test.

  method: the character string `"Kruskal-Wallis rank sum test"'.

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

_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 115-120.

_S_e_e _A_l_s_o:

     The Wilcoxon rank sum test (`wilcox.test') as the special case for
     two samples; `lm' together with `anova' for performing one-way
     location analysis under normality assumptions; with Student's t
     test (`t.test') as the special case for two samples.

_E_x_a_m_p_l_e_s:

     ## Hollander & Wolfe (1973), 116.
     ## Mucociliary efficiency from the rate of removal of dust in normal
     ##  subjects, subjects with obstructive airway disease, and subjects
     ##  with asbestosis.
     x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
     y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
     z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
     kruskal.test(list(x, y, z))
     ## Equivalently,
     x <- c(x, y, z)
     g <- factor(rep(1:3, c(5, 4, 5)),
                 labels = c("Normal subjects",
                            "Subjects with obstructive airway disease",
                            "Subjects with asbestosis"))
     kruskal.test(x, g)

