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-
- _F_r_i_e_d_m_a_n _R_a_n_k _S_u_m _T_e_s_t
-
- friedman.test(y, groups, blocks)
-
- _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 correspond-
- ing 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 correspond-
- ing elements of y if this is a vector;
- ignored if y is a matrix. If not a factor
- object, it is coerced to one.
-
- _D_e_s_c_r_i_p_t_i_o_n:
-
- 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.
-
-