mahaout                package:dprep                R Documentation

_M_u_l_t_i_v_a_r_i_a_t_e _o_u_t_l_i_e_r _d_e_t_e_c_t_i_o_n _t_h_r_o_u_g_h _t_h_e _b_o_x_p_l_o_t _o_f _t_h_e _M_a_h_a_l_a_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 multivariate outliers through the boxplot
     of the  Mahalanobis distance of all the instances.

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

     mahaout(data, nclass, plot = TRUE)

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

    data: name of the dataset

  nclass: number of the class to check for outliers

    plot: logical value. If plot=T a plot of the mahalanobis distance
          is drawn

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

     uses cov.rob function from the MASS library

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

     Returns a list of top outliers according to their Mahalanobis
     distance. Furthermore returns a list of all the instances ordered
     according to their Mahalanobis distance.

     If Plot=T returns a plot of the instances ranked by their
     Mahalanobis distance.

_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.

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

     'robout'

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

     #---- Detecting outliers using the Mahalanobis distance----
     data(bupa)
     mahaout(bupa,1)

