sbgcop.mcmc              package:sbgcop              R Documentation

_S_e_m_i_p_a_r_a_m_e_t_r_i_c _B_a_y_e_s_i_a_n _G_a_u_s_s_i_a_n _c_o_p_u_l_a _e_s_t_i_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     'sbgcop.mcmc' is used to semiparametrically estimate the
     parameters of a Gaussian copula. It can be used for posterior
     inference on the copula parameters, or for imputation of missing
     values in matrix-valued data.

_U_s_a_g_e:

     sbgcop.mcmc(Y, S0 = diag(dim(Y)[2]), n0 = dim(Y)[2] + 2, nsamp = 100,
       odens = max(1, round(nsamp/1000)), seed = 1, verb = TRUE)

_A_r_g_u_m_e_n_t_s:

       Y: an n x p matrix. Missing values are allowed.   

      S0: a p x p positive definite matrix 

      n0: a positive integer 

   nsamp: number of iterations of the Markov chain. 

   odens: output density: number of  iterations  between saved samples.            

    seed: an integer for the random seed

    verb: print progress of MCMC(TRUE/FALSE)? 

_D_e_t_a_i_l_s:

     This function produces MCMC samples from the posterior
     distribution of a correlation matrix, using a scaled 
     inverse-Wishart prior distribution and an extended rank
     likelihood. It also provides imputation for missing values  in a
     multivariate dataset.

_V_a_l_u_e:

     An object of class 'psgc' containing the following components: 

C.psamp : an array of size p x p x 'nsamp/odens', consisting of
          posterior samples of the correlation matrix.  

Y.pmean : the original datamatrix with imputed values replacing missing
          data   

    LPC : the log-probability of the latent variables at each saved
          sample. Used for diagnostic purposes.   

_A_u_t_h_o_r(_s):

     Peter Hoff

_R_e_f_e_r_e_n_c_e_s:

     http://www.stat.washington.edu/hoff/

_E_x_a_m_p_l_e_s:

     fit<-sbgcop.mcmc(swiss)
     summary(fit)
     plot(fit)