maxlof                 package:dprep                 R Documentation

_D_e_t_e_c_t_i_o_n _o_f _m_u_l_t_i_v_a_r_i_a_t_e _o_u_t_l_i_e_r_s _u_s_i_n_g _t_h_e _L_O_F _a_l_g_o_r_i_t_h_m

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

     A function that detects multivariate outliers using the local
     outlier factor for a matrix over a range of neighbors called
     Minpts.

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

     maxlof(data, name = "", minptsl = 10, minptsu = 20)

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

    data: dataset for outlier detection

    name: name to be given to the dataset, used in the graph title.

 minptsl: lower bound for the range of number of neighbors

 minptsu: upper bound for the range of number of neighbors

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

     Calls on the function "lofactor" to compute the local outlier
     factor for each number of neighbors in the range [minptsl,
     minptsu]. Also displays a plot of the factors for each observation
     of the dataset. In the plot, the user should seek to identify
     observations with large gaps between outlyingness measures. These
     would be the candidate for outliers.

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

maxlofactor: a vector containing the index of each observation of the
          dataset and the corresponding local outlier factor.

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

     Caroline Rodriguez

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

     Breuning, M., Kriegel, H., Ng, R.T, and Sander. J. (2000).  LOF:
     Identifying density-based local outliers. In Proceedings of the 
     ACM SIGMOD International Conference on Management of Data.

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

     #Detecting top 10 outliers in Breastw-class1 using the LOF algorithm
     data(breastw)
     breastw1.lof=maxlof(breastw[breastw[,10]==1,],name="",30,40)
     breastw1.lof[order(breastw1.lof,decreasing=TRUE)][1:10]

