hbk                  package:rrcov                  R Documentation

_H_a_w_k_i_n_s, _B_r_a_d_u, _K_a_s_s'_s _A_r_t_i_f_i_c_i_a_l _D_a_t_a

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

     Artificial Data Set generated by Hawkins, Bradu, and Kass (1984). 
     The data set consists of 75 observations in four dimensions  (one
     response and three explanatory variables). It provides a  good
     example of the masking effect. The first 14  observations are
     outliers, created in two groups: 1-10 and 11-14.  Only
     observations 12, 13 and 14 appear as outliers when using 
     classical methods, but can be easily unmasked using robust 
     distances computed by e.g. MCD - covMcd().

_U_s_a_g_e:

     data(hbk)

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

     A data frame with 75 observations on 4 variables, where the last
     variable is the dependent one.

     For convenience, the data sets 'hbk.x', a matrix with the three
     (independent) variables of the data frame, and 'hbk.y', the
     numeric vector giving the fourth (dependent) variable, are
     provided as well.

_S_o_u_r_c_e:

     Hawkins, D.M., Bradu, D., and Kass, G.V. (1984) Location of
     several outliers in multiple regression data using elemental sets.
     _Technometrics,_ *26*, 197-208.

_S_e_e _A_l_s_o:

     P. J. Rousseeuw and A. M. Leroy (1987)  _Robust Regression and
     Outlier Detection._ Wiley, p.94.

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

     data(hbk)
     plot(hbk.x)
     covMcd(hbk.x)
     summary(lm.hbk <- lm(hbk.y ~ hbk.x))

