## File Name: tam_pv_mcmc_postproc_ic.R
## File Version: 0.12
tam_pv_mcmc_postproc_ic <- function(parameter_samples, deviance_samples,
theta_samples_mean, AXsi, B, guess, beta, variance, group_index, G, Y,
resp, resp.ind, maxK, pv, resp_ind_bool )
{
nstud <- nrow(Y)
nitems <- ncol(resp)
D <- attr( pv, "D")
nplausible <- attr( pv, "nplausible")
#--- init ic vector
ic <- c()
#*******************
# inference based on marginal likelihood
like <- tam_pv_mcmc_compute_marginal_likelihood( pv=pv, AXsi=AXsi, B=B, guess=guess,
resp=resp, resp.ind=resp.ind, maxK=maxK, resp_ind_bool=resp_ind_bool )
ic$deviance <- -2*sum( log( like ) )
ic$n <- nstud
ic$Npars <- ic$np <- ncol(parameter_samples)
#-- compute all criteria
ic <- tam_mml_ic_criteria(ic=ic)
#*****************
# fully Bayesian inference
theta <- theta_samples_mean
#--- Dbar
ic$Dbar <- mean(deviance_samples)
#--- Dhat
like <- tam_pv_mcmc_evaluate_likelihood( theta=theta, AXsi=AXsi, B=B, guess=guess,
resp=resp, resp.ind=resp.ind, maxK=maxK, resp_ind_bool=resp_ind_bool )
ic$Dhat <- -2*sum( log(like) )
#--- pD
ic$pD <- ic$Dbar - ic$Dhat
#--- DIC
ic$DIC <- ic$Dhat + 2 * ic$pD
#--- OUTPUT
return(ic)
}
# z0 <- tamcat( label=" * rest", time0=z0, active=active)
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