

HairEyeColor {base}                          R Documentation

_H_a_i_r _a_n_d _E_y_e _C_o_l_o_r _o_f _S_t_a_t_i_s_t_i_c_s _S_t_u_d_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n_:

     Distribution of hair and eye color and sex in 592
     statistics students.

_U_s_a_g_e_:

     data(HairEyeColor)

_F_o_r_m_a_t_:

     A 3-dimensional array resulting from cross-tabulating
     592 observations on 3 variables.  The variables and
     their levels are as follows:

     No     Name     Levels
      1     Hair     Black, Brown, Red, Blond
      2     Eye      Brown, Blue, Hazel, Green
      3     Sex      Male, Female

_D_e_t_a_i_l_s_:

     This data set is useful for illustrating various tech-
     niques for the analysis of contingency tables, such as
     the standard chi-square test or, more generally, log-
     linear modelling, and graphical methods such as mosaic
     plots, sieve diagrams or association plots.

_R_e_f_e_r_e_n_c_e_s_:

     Snee, R. D. (1974), Graphical display of two-way con-
     tingency tables.  The American Statistician, 28, 9-12.

     Friendly, M. (1992), Graphical Methods for Categorical
     Data.  SAS User Group International Conference Proceed-
     ings, 17, 190-200.  <URL: http://hot-
     spur.psych.yorku.ca/SCS/sugi/sugi17-paper.html>

     Friendly, M. (1992), Mosaic displays for loglinear mod-
     els.  Proceedings of the Statistical Graphics Section,
     61-68.  American Statistical Association.  <URL:
     http://hotspur.psych.yorku.ca/SCS/Papers/asa92.html>

_E_x_a_m_p_l_e_s_:

     data(HairEyeColor)
     ## Full mosaic
     mosaicplot(HairEyeColor)
     ## Aggregate over sex:
     x <- apply(HairEyeColor, c(1, 2), sum)
     x
     mosaicplot(x, main = "Relation between hair and eye color")

