maxmult            package:hyperdirichlet            R Documentation

_E_s_t_i_m_a_t_i_o_n _o_f _t_h_e _m_u_l_t_i_v_a_r_i_a_t_e _b_e_t_a _d_i_s_t_r_i_b_u_t_i_o_n

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

     Gives a maximum likelihood estimate for the parameters of a
     Dirichlet distribution, on the basis of datapoints drawn from a
     multivariate beta distribution

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

     maxmult(M, start_a = NULL, give = FALSE, method = "nlm", ...)

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

       M: Integer matrix whose rows give multinomial observations

 start_a: Start point for optimization, with default 'NULL' being
          interpreted as Mosimann's formula 29

    give: Boolean, with default 'FALSE' meaning to return just the
          point estimate and 'TRUE' meaning to return all the output
          from the optimization routine

  method: Text string specifying the optimization routine to use. Two
          values coded:  default 'nlm' means to use 'nlm()' and 'optim'
          meaning to use 'optim()'; anything else means to return
          Mosimann's estimate (equation 29)

     ...: Further arguments passed to  'nlm()' or 'optim()'

_D_e_t_a_i_l_s:

     Finds the maximum likelihood estimate from the equation 7 of
     Mosimann 1962.

_N_o_t_e:

     The 'nlm()' function appears to be better suited to this problem
     than 'optim()'.

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

     Robin K. S. Hankin

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

     J. E. Mosimann 1962.  On the compound multinomial distribution,
     the multivariate beta-distribution, and correlations among
     proportions.  _Biometrika_, volume 49, numbers 1 and 2, pp65-82.

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

     'pollen'

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

     data(pollen)
     maxmult(pollen, start_a=c(51.81, 0.987, 5.332, 1.961))

