poissgamexch           package:LearnBayes           R Documentation

_L_o_g _p_o_s_t_e_r_i_o_r _o_f _P_o_i_s_s_o_n/_g_a_m_m_a _e_x_c_h_a_n_g_e_a_b_l_e _m_o_d_e_l

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

     Computes the log posterior density of log alpha and log mu for a
     Poisson/gamma exchangeable model

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

     poissgamexch(theta,datapar)

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

   theta: matrix of parameter values where each row represents a value
          of (log alpha, log mu)

 datapar: list with components data, a matrix with columns e and y, and
          z0, prior hyperparameter

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

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

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

     Jim Albert

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

     e=c(532,584,672,722,904)
     y=c(0,0,2,1,1)
     data=cbind(e,y)
     par1=c(-4,0)
     par2=c(-4,1)
     z0=.5
     datapar=list(data=data,z0=z0)
     theta=rbind(par1,par2)
     poissgamexch(theta,datapar)

