hypvol                package:mclust                R Documentation

_A_p_r_o_x_i_m_a_t_e _H_y_p_e_r_v_o_l_u_m_e _f_o_r _M_u_l_t_i_v_a_r_i_a_t_e _D_a_t_a

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

     Computes a simple approximation to the hypervolume of a
     multivariate data set.

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

     hypvol(data, reciprocal=FALSE)

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

    data: A numeric vector, matrix, or data frame of observations.
          Categorical variables are not allowed. If a matrix or data
          frame, rows correspond to observations and columns correspond
          to variables.  

reciprocal: A logical variable indicating whether or not the reciprocal
          hypervolume is desired rather than the hypervolume itself.
          The default is to return the hypervolume.   

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

     Computes the hypervolume by two methods: simple variable bounds
     and principal components, and returns the minimum value. Used to
     compute the default hypervolume parameter for the noise  component
     in

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

     A. Dasgupta and A. E. Raftery (1998). Detecting features in
     spatial point processes with clutter via model-based clustering. 
     _Journal of the American Statistical Association 93:294-302_. 

     C. Fraley and A.E. Raftery (1998). _Computer Journal 41:578-588_.

     C. Fraley and A. E. Raftery (2002). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_.

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

     'mclustBIC'

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

     hypvol(iris[,-5])

