DINA_FOHM

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

TP <- TPmat(K)
Omega_true <- rOmega(TP)
class_0 <- sample(1:2^K, N, replace = L)
Alphas_0 <- matrix(0,N,K)
for(i in 1:N){
 Alphas_0[i,] <- inv_bijectionvector(K,(class_0[i]-1))
}
Alphas <- sim_alphas(model="FOHM", Omega = Omega_true, N=N, L=L)
itempars_true <- matrix(runif(J*2,.1,.2), ncol=2)

Y_sim <- sim_hmcdm(model="DINA",Alphas,Q_matrix,Design_array,
                   itempars=itempars_true)

output_FOHM = hmcdm(Y_sim,Q_matrix,"DINA_FOHM",Design_array,100,30)
#> 0
output_FOHM
#> 
#> Model: DINA_FOHM 
#> 
#> Sample Size: 350
#> Number of Items: 
#> Number of Time Points: 
#> 
#> Chain Length: 100, burn-in: 50
summary(output_FOHM)
#> 
#> Model: DINA_FOHM 
#> 
#> Item Parameters:
#>  ss_EAP gs_EAP
#>  0.2268 0.1448
#>  0.1936 0.1967
#>  0.1645 0.2347
#>  0.1487 0.2092
#>  0.1489 0.1085
#>    ... 45 more items
#> 
#> Transition Parameters:
#>  [1] 0.02241 0.03004 0.03006 0.02264 0.02932 0.02900 0.05417 0.06629 0.12017
#> [10] 0.17819 0.15945 0.02662 0.06635 0.10660 0.02111 0.03757
#>    ... 15 more rows
#> 
#> Class Probabilities:
#>      pis_EAP
#> 0000  0.1670
#> 0001  0.2611
#> 0010  0.1588
#> 0011  0.2025
#> 0100  0.1458
#>    ... 11 more classes
#> 
#> Deviance Information Criterion (DIC): 18365.61 
#> 
#> Posterior Predictive P-value (PPP):
#> M1: 0.5084
#> M2:  0.49
#> total scores:  0.6293
a <- summary(output_FOHM)
head(a$ss_EAP)
#>           [,1]
#> [1,] 0.2268467
#> [2,] 0.1936234
#> [3,] 0.1644741
#> [4,] 0.1487108
#> [5,] 0.1488766
#> [6,] 0.1347755

AAR_vec <- numeric(L)
for(t in 1:L){
  AAR_vec[t] <- mean(Alphas[,,t]==a$Alphas_est[,,t])
}
AAR_vec
#> [1] 0.9450000 0.9450000 0.9685714 0.9850000 0.9878571

PAR_vec <- numeric(L)
for(t in 1:L){
  PAR_vec[t] <- mean(rowSums((Alphas[,,t]-a$Alphas_est[,,t])^2)==0)
}
PAR_vec
#> [1] 0.7971429 0.8228571 0.8885714 0.9485714 0.9514286

a$DIC
#>              Transition Response_Time Response    Joint    Total
#> D_bar          2154.499            NA 14475.25 1232.942 17862.69
#> D(theta_bar)   2054.542            NA 14122.52 1182.712 17359.77
#> DIC            2254.457            NA 14827.98 1283.172 18365.61
head(a$PPP_total_scores)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.36 0.88 0.22 0.40 0.28
#> [2,] 0.74 0.78 0.88 1.00 0.92
#> [3,] 0.82 0.90 0.40 0.10 0.96
#> [4,] 0.50 0.72 0.02 0.82 0.58
#> [5,] 0.38 0.62 0.52 0.90 0.54
#> [6,] 0.32 1.00 1.00 0.82 0.80
head(a$PPP_item_means)
#> [1] 0.54 0.42 0.62 0.50 0.50 0.46
head(a$PPP_item_ORs)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]   NA 0.88 0.54 0.50 0.76 0.88 0.76 0.62 0.28  0.56  0.20  0.64  0.08  0.24
#> [2,]   NA   NA 0.80 0.14 0.68 0.92 0.94 0.94 0.32  0.82  0.56  0.38  0.38  0.98
#> [3,]   NA   NA   NA 0.34 0.36 0.28 0.54 0.36 0.72  0.50  0.58  0.68  1.00  0.76
#> [4,]   NA   NA   NA   NA 0.32 0.18 0.64 0.56 0.44  0.28  0.40  0.28  0.90  0.12
#> [5,]   NA   NA   NA   NA   NA 0.44 0.64 0.68 0.36  0.40  0.42  0.30  0.78  0.34
#> [6,]   NA   NA   NA   NA   NA   NA 0.60 0.40 0.58  0.44  0.62  0.48  0.10  0.26
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]  0.98  0.88  0.14  0.60  0.40  0.40  0.92  0.96  0.22  0.08  0.42  0.28
#> [2,]  0.44  0.74  0.40  0.52  0.56  0.52  0.32  0.60  0.92  0.18  0.34  0.08
#> [3,]  0.96  0.94  0.96  0.44  0.84  0.68  0.54  0.08  0.34  0.76  0.78  0.06
#> [4,]  0.88  0.40  0.20  0.20  0.02  0.74  0.38  0.52  0.64  0.88  1.00  0.04
#> [5,]  0.98  0.98  0.12  0.40  0.58  0.38  0.84  0.84  0.08  0.94  0.24  0.66
#> [6,]  0.84  0.78  0.12  0.34  0.18  0.30  0.96  0.78  0.62  0.82  0.42  0.44
#>      [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,]  1.00  0.10  0.30  0.16  0.86  0.10  0.22  0.02  0.54  0.50  0.56  0.98
#> [2,]  0.68  0.72  0.18  0.26  0.00  0.10  0.32  0.52  0.12  0.64  0.48  0.32
#> [3,]  0.18  0.36  0.12  0.04  0.18  0.84  0.08  0.78  0.56  0.84  0.70  0.48
#> [4,]  0.64  0.78  0.92  0.74  0.92  0.80  0.38  0.42  0.98  0.98  0.86  0.72
#> [5,]  0.64  0.48  0.08  0.18  0.18  0.10  0.06  0.14  0.82  0.34  0.20  0.76
#> [6,]  0.60  0.56  0.60  0.06  0.06  0.44  0.24  0.00  0.58  0.36  0.00  0.62
#>      [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,]  0.32  0.88  0.24  0.44  0.74  0.78  0.02  1.00  0.22  0.76  0.50  0.22
#> [2,]  0.96  0.12  0.10  0.94  1.00  0.28  0.06  0.66  0.56  0.66  0.28  0.44
#> [3,]  0.34  0.62  0.44  0.06  0.76  0.60  0.16  0.52  0.18  0.74  0.92  0.18
#> [4,]  0.66  0.88  0.02  0.60  0.32  0.72  0.54  0.14  0.02  0.12  0.92  0.02
#> [5,]  0.20  0.72  0.10  0.60  0.86  0.40  0.22  0.34  0.20  0.88  0.14  0.36
#> [6,]  0.42  0.14  0.18  0.52  0.56  0.44  0.28  0.70  0.36  0.54  0.74  0.00