

awe(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_.

_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.

_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.

_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)

