weibullregpost          package:LearnBayes          R Documentation

_L_o_g _p_o_s_t_e_r_i_o_r _o_f _a _W_e_i_b_u_l_l _p_r_o_p_o_r_t_i_o_n_a_l _o_d_d_s _m_o_d_e_l _f_o_r _s_u_r_v_i_v_a_l _d_a_t_a

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

     Computes the log posterior density of (log sigma, mu, beta) for a
     Weibull proportional odds regression model

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

     weibullregpost(theta,data)

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

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

    data: data matrix with columns survival time, censoring variable,
          and covariate matrix

_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:

     data(chemotherapy)
     attach(chemotherapy)
     d=cbind(time,status,treat-1,age)
     par1=c(-.6,11,.6,0)
     par2=c(-.6,11,.6,.1)
     theta=rbind(par1,par2)
     weibullregpost(theta,d)

