

loglik(mclust)                               R Documentation

_L_o_g_l_i_k_e_l_i_h_o_o_d _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_:

     loglik(tree, data, Vinv)

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

    tree: an `"mhtree"' object.

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

    Vinv: approximate recoprocal hypervolume of the region
          from which the data is drawn.  The default (for
          those methods that need this quantity) is deter-
          mined by the function `hypvol'.

_V_a_l_u_e_:

     the loglikelihood corresponding to the initial parti-
     tion and to each stage of merging in hierarchical clus-
     tering, together with the following attribute:

  nmerge: the number of clusters merged at each stage.

_D_E_S_C_R_I_P_T_I_O_N_:

     Gives the loglikelihood for each stage of model-based
     hierarchical clustering.

_N_O_T_E_S_:

     The value given is equal to the loglikelihood up to an
     additive constant.  For those models in which they
     arise, indeterminate terms are assigned the value
     `-k*log(volume)', where `k' is the number of observa-
     tions associated with the term.  If you scaled your
     data before using `mhtree', be sure to use the same
     scaling when supplying the data to `loglik'.

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

     `mhtree', `awe'

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

     data(iris)
     loglik(mhtree(iris[,1:4]),iris[,1:4])

