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
#>  0.21836 0.11025
#>  0.13437 0.14918
#>  0.19428 0.21137
#>  0.08312 0.08937
#>  0.17487 0.13298
#>    ... 45 more items
#> 
#> Transition Parameters:
#>  [1] 0.07880 0.07513 0.08433 0.08808 0.05985 0.09191 0.04126 0.01833 0.03593
#> [10] 0.03816 0.10668 0.04323 0.02646 0.08144 0.04308 0.08732
#>    ... 15 more rows
#> 
#> Class Probabilities:
#>      pis_EAP
#> 0000  0.2018
#> 0001  0.1922
#> 0010  0.2032
#> 0011  0.1619
#> 0100  0.1755
#>    ... 11 more classes
#> 
#> Deviance Information Criterion (DIC): 19033.31 
#> 
#> Posterior Predictive P-value (PPP):
#> M1: 0.5208
#> M2:  0.49
#> total scores:  0.6266
a <- summary(output_FOHM)
head(a$ss_EAP)
#>            [,1]
#> [1,] 0.21835806
#> [2,] 0.13436923
#> [3,] 0.19428468
#> [4,] 0.08312328
#> [5,] 0.17486726
#> [6,] 0.15140283

AAR_vec <- numeric(L)
for(t in 1:L){
  AAR_vec[t] <- mean(Alphas[,,t]==a$Alphas_est[,,t])
}
AAR_vec
#> [1] 0.9342857 0.9471429 0.9735714 0.9864286 0.9892857

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] 0.7685714 0.8114286 0.8971429 0.9514286 0.9600000

a$DIC
#>              Transition Response_Time Response    Joint    Total
#> D_bar          2268.087            NA 14911.24 1239.530 18418.85
#> D(theta_bar)   2180.982            NA 14423.95 1199.473 17804.40
#> DIC            2355.191            NA 15398.53 1279.587 19033.31
head(a$PPP_total_scores)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.62 0.18 1.00 0.92 0.50
#> [2,] 0.18 0.50 0.92 0.46 0.30
#> [3,] 0.40 0.86 0.56 1.00 1.00
#> [4,] 0.76 0.94 0.40 0.90 0.50
#> [5,] 0.80 0.84 0.40 0.84 0.22
#> [6,] 0.18 0.94 0.28 0.82 1.00
head(a$PPP_item_means)
#> [1] 0.60 0.60 0.46 0.56 0.68 0.42
head(a$PPP_item_ORs)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]   NA 0.92 0.98 0.40 0.68 0.84 0.80 0.20 0.84  0.30  0.06  0.62  0.00  0.44
#> [2,]   NA   NA 0.22 0.68 0.74 0.38 0.44 1.00 0.74  0.76  0.66  0.86  0.88  0.82
#> [3,]   NA   NA   NA 0.52 0.98 0.50 0.32 0.84 0.50  0.72  0.62  0.52  0.74  0.94
#> [4,]   NA   NA   NA   NA 0.58 0.48 0.86 0.92 0.32  0.66  0.42  0.14  0.14  0.62
#> [5,]   NA   NA   NA   NA   NA 0.58 0.62 0.48 0.98  0.74  0.56  0.02  0.24  0.34
#> [6,]   NA   NA   NA   NA   NA   NA 0.54 0.68 0.46  0.46  0.22  0.62  0.54  0.30
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]  0.50  0.54  0.60  0.56  0.04  0.44  0.24  0.40  0.10  0.76  0.02  0.92
#> [2,]  0.94  0.90  0.62  0.80  0.80  0.68  0.38  0.18  0.62  0.64  0.06  0.78
#> [3,]  0.26  0.84  0.90  0.54  0.20  0.60  0.00  0.60  0.38  0.30  0.74  0.36
#> [4,]  0.48  0.74  0.10  0.24  0.32  0.32  0.18  0.38  0.48  0.96  0.70  0.54
#> [5,]  0.06  0.54  0.18  0.50  0.32  0.44  0.40  0.78  0.98  0.58  0.78  0.70
#> [6,]  0.30  0.20  0.78  0.06  0.16  0.00  0.06  0.58  0.50  0.74  0.14  0.78
#>      [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,]  0.86  0.04  0.62  0.12  0.62  0.50  0.28  0.60  0.08  0.34  0.62  0.88
#> [2,]  0.38  0.44  0.28  0.70  0.50  0.24  0.78  0.36  0.08  0.24  0.48  0.62
#> [3,]  0.78  0.08  0.18  0.32  0.14  0.22  0.92  0.28  0.34  0.20  0.82  0.32
#> [4,]  0.06  0.20  0.16  0.52  0.28  0.14  0.66  0.20  0.16  0.28  0.94  0.40
#> [5,]  0.92  0.70  0.84  0.88  0.96  0.94  0.72  0.68  0.44  0.56  0.76  0.72
#> [6,]  0.62  0.14  0.34  0.32  0.16  0.86  0.74  0.82  0.12  0.84  0.56  0.52
#>      [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,]  0.86  0.36  0.64  0.10  0.68  0.96  0.32  0.90  0.34  0.38  0.74  0.84
#> [2,]  0.20  0.12  0.12  0.84  0.84  0.20  0.40  0.78  0.38  0.00  0.72  0.16
#> [3,]  0.70  0.82  0.82  0.70  0.30  1.00  0.60  0.32  0.44  0.22  0.58  0.40
#> [4,]  0.22  0.52  0.80  0.80  0.44  0.04  0.48  0.78  0.24  0.46  0.82  0.60
#> [5,]  0.82  0.46  0.20  0.54  0.42  0.54  0.62  0.96  0.48  0.40  0.82  0.42
#> [6,]  0.34  0.40  0.60  0.96  0.82  0.56  0.50  0.72  0.82  0.86  0.98  0.92