robout                 package:dprep                 R Documentation

_O_u_t_l_i_e_r _D_e_t_e_c_t_i_o_n _w_i_t_h _R_o_b_u_s_t _M_a_h_a_l_o_n_o_b_i_s _d_i_s_t_a_n_c_e

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

     This function finds out the outliers of a dataset using  robust
     versions of the Mahalanobis distance.

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

     robout(data, nclass, meth = c("mve", "mcd"), rep = 10, 
     plot = TRUE)

_A_r_g_u_m_e_n_t_s:

    data: the dataset for which outlier detection will be carried out.

  nclass: integer value that represents the class to detect for
          outliers

    meth: method used to compute the Mahalanobis distance,
          "mve"=minimum volume estimator, "mcd"=minimum covariance
          determinant

     rep: number of repetitions

    plot: boolean value to turn on or off the scatter plot of the 
          Mahalanobis distances

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

     Requires uses cov.rob function from the MASS library.

_V_a_l_u_e:

    top1: 

  topout: index of observations identified as possible outliers  by
          outlyingness measure

   outme: index of observations and their outlyingness measures

_A_u_t_h_o_r(_s):

     Edgar Acuna

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

     Rousseeuw, P, and Leroy, A. (1987). Robust Regression and outlier
     detection. John Wiley & Sons. New York.

     Atkinson, A. (1994). Fast very robust methods for the detection of
      multiple outliers. Journal of the American Statistical
     Association, 89:1329-1339.

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

     #---- Outlier Detection in bupa-class 1 using MCD
     data(bupa)
     robout(bupa,1,"mcd")

