N=length(Test_versions)J=nrow(Q_matrix)K=ncol(Q_matrix)L=nrow(Test_order)

class_0<-sample(1:2^K, N,replace = L)Alphas_0<-matrix(0,N,K)for(iin1:N){  Alphas_0[i,]<-inv_bijectionvector(K,(class_0[i]-1))}thetas_true=rnorm(N,0,1)tausd_true=0.5taus_true=rnorm(N,0,tausd_true)G_version=3phi_true=0.8lambdas_true<-c(-2,1.6, .4, .055)# empirical from Wang 2017Alphas<-sim_alphas(model="HO_sep",lambdas=lambdas_true,thetas=thetas_true,Q_matrix=Q_matrix,Design_array=Design_array)table(rowSums(Alphas[,,5])-rowSums(Alphas[,,1]))# used to see how much transition has taken place#>#>   0   1   2   3   4#>  66  52  80 121  31itempars_true<-matrix(runif(J*2,.1,.2),ncol=2)RT_itempars_true<-matrix(NA,nrow=J,ncol=2)RT_itempars_true[,2]<-rnorm(J,3.45,.5)RT_itempars_true[,1]<-runif(J,1.5,2)Y_sim<-sim_hmcdm(model="DINA",Alphas,Q_matrix,Design_array,itempars=itempars_true)L_sim<-sim_RT(Alphas,Q_matrix,Design_array,RT_itempars_true,taus_true,phi_true,G_version)

output_HMDCM_RT_sep=hmcdm(Y_sim,Q_matrix,"DINA_HO_RT_sep",Design_array,100,30,Latency_array = L_sim,G_version = G_version,theta_propose =2,deltas_propose =c(.45,.35,.25,.06))#> 0output_HMDCM_RT_sep#>#> Model: DINA_HO_RT_sep#>#> Sample Size: 350#> Number of Items:#> Number of Time Points:#>#> Chain Length: 100, burn-in: 50summary(output_HMDCM_RT_sep)#>#> Model: DINA_HO_RT_sep#>#> Item Parameters:#>   ss_EAP  gs_EAP#>  0.16149 0.19939#>  0.09851 0.08643#>  0.21014 0.17458#>  0.18473 0.23380#>  0.11463 0.20836#>    ... 45 more items#>#> Transition Parameters:#>    lambdas_EAP#> λ0    -1.85612#> λ1     1.80213#> λ2     0.23449#> λ3     0.08619#>#> Class Probabilities:#>      pis_EAP#> 0000  0.1441#> 0001  0.1981#> 0010  0.1769#> 0011  0.2421#> 0100  0.1737#>    ... 11 more classes#>#> Deviance Information Criterion (DIC): 157110.6#>#> Posterior Predictive P-value (PPP):#> M1: 0.5172#> M2:  0.49#> total scores:  0.6265a<-summary(output_HMDCM_RT_sep)head(a$ss_EAP)#>            [,1]#> [1,] 0.16149025#> [2,] 0.09850913#> [3,] 0.21013584#> [4,] 0.18473048#> [5,] 0.11463297#> [6,] 0.12763509

(cor_thetas<-cor(thetas_true,a$thetas_EAP))#>           [,1]#> [1,] 0.8073238(cor_taus<-cor(taus_true,a$response_times_coefficients$taus_EAP))#>           [,1]#> [1,] 0.9869721(cor_ss<-cor(as.vector(itempars_true[,1]),a$ss_EAP))#>           [,1]#> [1,] 0.5477734(cor_gs<-cor(as.vector(itempars_true[,2]),a$gs_EAP))#>           [,1]#> [1,] 0.7695372AAR_vec<-numeric(L)for(tin1:L){  AAR_vec[t]<-mean(Alphas[,,t]==a$Alphas_est[,,t])}AAR_vec#> [1] 0.9221429 0.9435714 0.9592857 0.9685714 0.9614286PAR_vec<-numeric(L)for(tin1:L){  PAR_vec[t]<-mean(rowSums((Alphas[,,t]-a$Alphas_est[,,t])^2)==0)}PAR_vec#> [1] 0.7485714 0.8085714 0.8514286 0.8857143 0.8628571

a$DIC#>              Transition Response_Time Response    Joint    Total#> D_bar          2205.921      135843.4 15042.20 3067.784 156159.4#> D(theta_bar)   1905.363      135402.1 14870.05 3030.558 155208.1#> DIC            2506.479      136284.8 15214.35 3105.011 157110.6head(a$PPP_total_scores)#>      [,1] [,2] [,3] [,4] [,5]#> [1,] 0.82 0.16 0.08 0.94 1.00#> [2,] 0.54 0.78 0.10 0.96 0.78#> [3,] 0.76 0.42 0.38 0.58 0.86#> [4,] 0.48 0.76 0.08 1.00 1.00#> [5,] 0.66 0.74 0.68 0.78 0.96#> [6,] 0.50 0.70 0.80 0.98 0.12head(a$PPP_item_means)#> [1] 0.50 0.38 0.56 0.46 0.42 0.32head(a$PPP_item_ORs)#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]#> [1,]   NA 0.22 0.96 0.94 0.48 0.26 0.36 0.38 0.82  0.36  0.14  0.18  0.68  0.20#> [2,]   NA   NA 0.60 0.82 0.24 0.64 0.18 0.72 0.82  0.44  0.44  0.68  1.00  0.58#> [3,]   NA   NA   NA 0.64 0.82 0.92 0.72 0.94 0.78  0.22  0.70  0.02  0.64  0.86#> [4,]   NA   NA   NA   NA 1.00 0.46 0.66 0.90 0.78  0.92  0.66  0.60  0.84  0.68#> [5,]   NA   NA   NA   NA   NA 0.12 0.40 0.62 0.84  0.68  0.96  0.28  1.00  0.12#> [6,]   NA   NA   NA   NA   NA   NA 0.50 0.86 0.74  0.60  0.58  0.32  0.86  0.80#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]#> [1,]  0.08  0.06  0.16  0.42  0.16  0.72  0.10  0.64  0.42  0.48  0.68  0.30#> [2,]  0.80  0.54  0.46  0.54  0.72  0.84  0.62  0.76  0.82  0.54  0.44  0.24#> [3,]  0.50  0.16  0.16  0.56  0.46  0.90  0.22  0.94  0.48  0.70  0.56  0.42#> [4,]  0.78  0.64  0.22  0.32  0.52  0.56  0.92  0.82  0.78  0.62  0.96  0.00#> [5,]  0.40  0.24  0.16  0.36  0.76  0.78  0.72  0.40  0.08  0.98  0.90  0.62#> [6,]  0.22  0.62  0.02  0.86  0.78  0.78  0.76  0.60  0.94  0.16  0.94  0.84#>      [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]#> [1,]  1.00  0.04  0.02  0.34  0.12  0.00  0.40  0.50  0.12  0.20  0.16  0.18#> [2,]  0.36  0.54  0.68  0.16  0.96  0.42  0.70  0.34  0.44  0.04  0.48  0.68#> [3,]  0.74  0.48  0.54  0.68  0.38  0.48  0.50  0.80  0.98  0.58  0.98  0.40#> [4,]  0.58  0.18  0.82  0.76  0.48  0.56  0.38  0.24  0.10  0.54  0.96  0.58#> [5,]  0.56  0.48  0.14  0.76  0.98  0.78  0.56  0.46  0.82  0.86  0.66  0.96#> [6,]  0.64  0.92  0.66  0.64  0.42  0.16  0.54  0.62  0.14  0.04  0.68  0.64#>      [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]#> [1,]  0.48  0.10  0.26  1.00  0.72  0.10  0.26  0.24  0.54  0.44  0.84  0.12#> [2,]  0.72  0.50  0.76  0.62  0.12  0.80  0.76  0.86  0.24  0.92  0.50  0.54#> [3,]  0.76  0.28  0.78  0.86  0.96  0.20  0.68  0.96  0.12  0.38  0.80  0.84#> [4,]  0.22  0.70  0.60  0.66  0.78  0.96  0.68  0.54  0.56  0.22  0.66  0.48#> [5,]  0.26  0.62  0.98  0.90  0.44  0.66  0.68  0.42  0.36  0.62  0.80  0.48#> [6,]  0.64  0.22  0.82  0.56  0.26  0.36  0.90  0.70  0.32  0.04  0.64  0.68library(bayesplot)#> This is bayesplot version 1.14.0#> - Online documentation and vignettes at mc-stan.org/bayesplot#> - bayesplot theme set to bayesplot::theme_default()#>    * Does _not_ affect other ggplot2 plots#>    * See ?bayesplot_theme_set for details on theme settingpp_check(output_HMDCM_RT_sep,type="total_latency")