friedman.test             package:ctest             R Documentation

_F_r_i_e_d_m_a_n _R_a_n_k _S_u_m _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     Performs a Friedman rank sum test with unreplicated blocked data.

_U_s_a_g_e:

     friedman.test(y, groups, blocks, ...)
     friedman.test(formula, data, subset, na.action, ...)

_A_r_g_u_m_e_n_t_s:

       y: either a numeric vector of data values, or a data matrix.

  groups: a vector giving the group for the corresponding elements of
          `y' if this is a vector;  ignored if `y' is a matrix.  If not
          a factor object, it is coerced to one.

  blocks: a vector giving the block for the corresponding elements of
          `y' if this is a vector;  ignored if `y' is a matrix.  If not
          a factor object, it is coerced to one.

 formula: a formula of the form `a ~ b | c', where `a', `b' and `c'
          give the data values and corresponding groups and blocks,
          respectively.

    data: an optional data frame containing the variables in the model
          formula.

  subset: an optional vector specifying a subset of observations to be
          used.

na.action: a function which indicates what should happen when the data
          contain `NA's.  Defaults to `getOption("na.action")'.

     ...: further arguments to be passed to or from methods.

_D_e_t_a_i_l_s:

     `friedman.test' can be used for analyzing unreplicated complete
     block designs (i.e., there is exactly one observation in `y' for
     each combination of levels of `groups' and `blocks') where the
     normality assumption may be violated.

     The null hypothesis is that apart from an effect of `blocks', the
     location parameter of `y' is the same in each of the `groups'. 

     If `y' is a matrix, `groups' and `blocks' are obtained from the
     column and row indices, respectively.  `NA''s are not allowed in
     `groups' or `blocks';  if `y' contains `NA''s, corresponding
     blocks are removed.

_V_a_l_u_e:

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

statistic: the value of Friedman's chi-squared 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 `"Friedman 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 139-146.

_S_e_e _A_l_s_o:

     `quade.test'.

_E_x_a_m_p_l_e_s:

     ## Hollander & Wolfe (1973), p. 140ff.
     ## Comparison of three methods (``round out'', ``narrow angle'', and
     ##  ``wide angle'') for rounding first base.  For each of 18 players
     ##  and the three method, the average time of two runs from a point on
     ##  the first base line 35ft from home plate to a point 15ft short of
     ##  second base is recorded.
     RoundingTimes <-
     matrix(c(5.40, 5.50, 5.55,
              5.85, 5.70, 5.75,
              5.20, 5.60, 5.50,
              5.55, 5.50, 5.40,
              5.90, 5.85, 5.70,
              5.45, 5.55, 5.60,
              5.40, 5.40, 5.35,
              5.45, 5.50, 5.35,
              5.25, 5.15, 5.00,
              5.85, 5.80, 5.70,
              5.25, 5.20, 5.10,
              5.65, 5.55, 5.45,
              5.60, 5.35, 5.45,
              5.05, 5.00, 4.95,
              5.50, 5.50, 5.40,
              5.45, 5.55, 5.50,
              5.55, 5.55, 5.35,
              5.45, 5.50, 5.55,
              5.50, 5.45, 5.25,
              5.65, 5.60, 5.40,
              5.70, 5.65, 5.55,
              6.30, 6.30, 6.25),
            nr = 22,
            byrow = TRUE,
            dimnames = list(1 : 22,
                            c("Round Out", "Narrow Angle", "Wide Angle")))
     friedman.test(RoundingTimes)
     ## => strong evidence against the null that the methods are equivalent
     ##    with respect to speed

     data(warpbreaks)
     wb <- aggregate(warpbreaks$breaks,
                     by = list(w = warpbreaks$wool,
                               t = warpbreaks$tension),
                     FUN = mean)
     wb
     friedman.test(wb$x, wb$w, wb$t)
     friedman.test(x ~ w | t, data = wb)

