hbk                package:robustbase                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.

     _X_1 x[,1]

     _X_2 x[,2]

     _X_3 x[,3]

     _Y y

_N_o_t_e:

     This data set is also available in package 'wle' as 'artificial'.

_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.

     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)
     summary(lm.hbk <- lm(Y ~ ., data = hbk))

     hbk.x <- data.matrix(hbk[, 1:3])
     (cHBK <- covMcd(hbk.x))

