awe                  package:mclust                  R Documentation

_A_p_p_r_o_x_i_m_a_t_e _w_e_i_g_h_t _o_f _e_v_i_d_e_n_c_e _f_o_r _m_o_d_e_l-_b_a_s_e_d _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r_i_n_g.

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

     Computes a Bayesian criterion for assessing the number of clusters
     present in the data.

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

     awe(tree, data)

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

    tree: an `"mhtree"' object. 

    data: the data used to produce the `"mhtree"' object. 

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

     the approximate weight of evidence for each possible stage of
     merging.

_N_O_T_E_S:

     Since `"mhtree"' allows stopping and starting at any stage, the
     result will contain NAs for those stages that have  been
     eliminated.  If you scaled your data before using `mhtree', be
     sure to use the same scaling when supplying the data to `awe'.

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

     J. D. Banfield and A. E. Raftery, Model-based Gaussian and
     non-Gaussian Clustering, Biometrics, 49:803-821 (1993).

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

     `mhtree', `loglik'

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

     data(iris)
     iris.m _ iris[,1:4]
     awe.val <- awe(mhtree(iris.m), iris.m)
     plot(awe.val)

