howardprior            package:LearnBayes            R Documentation

_L_o_g_a_r_i_t_h_m _o_f _H_o_w_a_r_d'_s _d_e_p_e_n_d_e_n_t _p_r_i_o_r _f_o_r _t_w_o _p_r_o_p_o_r_t_i_o_n_s

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

     Computes the logarithm of a dependent prior on two proportions
     proposed by Howard in a Statistical Science paper in 1998.

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

     howardprior(xy,par)

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

      xy: matrix of parameter values where each row represents a value
          of the proportions (p1, p2)

     par: vector containing parameter values alpha, beta, gamma, delta,
          sigma

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

     vector of values of the log posterior where each value corresponds
     to each row of the parameters in xy

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

     Jim Albert

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

     par=c(1,1,1,1,2)
     p1=array(c(.1, .5),c(1,2))
     p2=array(c(.3, .8),c(1,2))
     xy=cbind(p1,p2)
     howardprior(xy,par)

