friedman.test             package:stats             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, ...)

     ## Default S3 method:
     friedman.test(y, groups, blocks, ...)

     ## S3 method for class 'formula':
     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)

