outlier                 package:SAGx                 R Documentation

_I_d_e_n_t_i_f_y _o_u_t_l_i_e_r_s _i_n _t_h_e _m_u_l_t_i_v_a_r_i_a_t_e _d_i_s_t_r_i_b_u_t_i_o_n

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

     A PCA model is fitted to data and two statistics as measures of
     extremity are calculated. These are the Hotelling t-square and
     DMODX, the first is a measure of how far away from the centre of
     the projection subspace the projection of the observation is. The
     second one measures how remote from the projection the actual
     observation is. SVD is done directly on the data matrix. The
     number of significant dimensions is defined as the number of
     eigenvalues greater than 1. Typically arrays are in different
     columns.

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

     outlier(M)

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

       M: matrix

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

     Dataframe with columns Hotelling and DMODX

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

     Per Broberg

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

     Jackson, J.E. (1991) _A User's Guide to Principal Components_.
     Wiley

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

     ## Not run: 
     # not run
     ut<-outlier(M)
     #[1] "The number of significant dimensions is 19"
     colnames(ut)
     #[1] "Hotelling" "DMODX"  
     ## End(Not run)

