DPpsBF               package:DPpackage               R Documentation

_C_o_m_p_u_t_e_s _P_s_e_u_d_o _B_a_y_e_s _F_a_c_t_o_r_s _f_r_o_m _D_P_p_a_c_k_a_g_e _o_u_t_p_u_t

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

     This function computes Pseudo Bayes Factors from DPpackage output.

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

     DPpsBF(...)

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

     ...: DPpackage output objects. These have to be of the same class.

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

     ## Not run: 
         # Respiratory Data Example

           data(indon)
           attach(indon)

           baseage2<-baseage**2
           follow<-age-baseage
           follow2<-follow**2 

         # Prior information

           beta0<-rep(0,9)
           Sbeta0<-diag(1000,9)
           tinv<-diag(1,1)
           prior<-list(a0=2,b0=0.1,nu0=4,tinv=tinv,mub=rep(0,1),Sb=diag(1000,1),
                       beta0=beta0,Sbeta0=Sbeta0)

         # Initial state
           state <- NULL

         # MCMC parameters

           nburn<-5
           nsave<-100
           nskip<-5
           ndisplay<-100
           mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay)

         # Fit the Probit model
           fit1<-DPglmm(fixed=infect~gender+height+cosv+sinv+xero+baseage+baseage2+
                        follow+follow2,random=~1|id,family=binomial(probit),
                        prior=prior,mcmc=mcmc,state=state,status=TRUE)

         # Fit the Logit model
           fit2<-DPglmm(fixed=infect~gender+height+cosv+sinv+xero+baseage+baseage2+
                        follow+follow2,random=~1|id,family=binomial(logit),
                        prior=prior,mcmc=mcmc,state=state,status=TRUE)

         # Model comparison
           DPpsBF(fit1,fit2)

     ## End(Not run)

