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
#>  0.1576 0.08557
#>  0.1621 0.09821
#>  0.1500 0.23800
#>  0.1490 0.24876
#>  0.1213 0.13764
#>    ... 45 more items
#> 
#> Transition Parameters:
#>  [1] 0.009651 0.135182 0.056938 0.086995 0.029737 0.019838 0.023920 0.039728
#>  [9] 0.033845 0.150820 0.038498 0.028707 0.167434 0.020255 0.130881 0.027574
#>    ... 15 more rows
#> 
#> Class Probabilities:
#>      pis_EAP
#> 0000  0.1973
#> 0001  0.1916
#> 0010  0.1761
#> 0011  0.1881
#> 0100  0.1710
#>    ... 11 more classes
#> 
#> Deviance Information Criterion (DIC): 18795.49 
#> 
#> Posterior Predictive P-value (PPP):
#> M1: 0.5172
#> M2:  0.49
#> total scores:  0.626
a <- summary(output_FOHM)
head(a$ss_EAP)
#>           [,1]
#> [1,] 0.1575805
#> [2,] 0.1620598
#> [3,] 0.1499780
#> [4,] 0.1490318
#> [5,] 0.1212823
#> [6,] 0.1279858

AAR_vec <- numeric(L)
for(t in 1:L){
  AAR_vec[t] <- mean(Alphas[,,t]==a$Alphas_est[,,t])
}
AAR_vec
#> [1] 0.9371429 0.9442857 0.9692857 0.9871429 0.9950000

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] 0.7800000 0.8057143 0.8914286 0.9542857 0.9800000

a$DIC
#>              Transition Response_Time Response    Joint    Total
#> D_bar          2222.446            NA 14690.59 1264.035 18177.07
#> D(theta_bar)   2163.493            NA 14170.74 1224.419 17558.65
#> DIC            2281.400            NA 15210.44 1303.651 18795.49
head(a$PPP_total_scores)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.50 0.50 0.86 0.18 0.02
#> [2,] 0.86 0.50 0.30 0.84 0.44
#> [3,] 0.16 0.50 0.78 0.72 0.92
#> [4,] 0.64 0.70 0.18 0.74 1.00
#> [5,] 0.90 0.10 1.00 1.00 0.90
#> [6,] 0.88 0.22 0.18 0.14 0.52
head(a$PPP_item_means)
#> [1] 0.42 0.46 0.64 0.36 0.60 0.46
head(a$PPP_item_ORs)
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]   NA 0.66 0.16 0.36 0.48 0.58 0.38 0.64 0.46  0.82  0.18  0.10  0.14  0.14
#> [2,]   NA   NA 0.44 0.44 0.76 0.34 0.76 0.40 0.08  0.36  0.72  0.54  0.16  0.42
#> [3,]   NA   NA   NA 0.12 0.26 0.82 0.62 0.62 0.44  0.60  0.70  0.04  0.96  0.48
#> [4,]   NA   NA   NA   NA 0.32 0.26 0.70 0.40 0.54  0.76  0.34  0.38  0.30  0.86
#> [5,]   NA   NA   NA   NA   NA 0.36 0.08 0.60 0.54  0.26  0.96  0.68  0.68  0.70
#> [6,]   NA   NA   NA   NA   NA   NA 0.34 0.90 0.18  0.38  0.48  0.02  0.22  0.10
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,]  0.50  0.22  0.36  0.10  0.06  0.44  0.24  0.00  0.82  0.44  0.14  0.08
#> [2,]  0.60  0.92  0.02  0.42  0.22  0.70  0.18  0.82  0.74  0.08  0.18  0.48
#> [3,]  0.76  0.40  0.62  1.00  0.46  0.76  0.30  0.42  0.94  0.54  0.74  0.70
#> [4,]  0.78  0.66  0.22  0.12  0.66  0.66  0.14  0.90  0.04  0.28  0.12  0.64
#> [5,]  0.28  0.18  0.18  0.54  0.08  0.72  0.14  0.36  0.98  0.64  0.00  0.36
#> [6,]  0.20  0.38  0.18  0.22  0.22  0.40  0.04  0.30  0.58  0.10  0.02  0.76
#>      [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,]  0.32  0.38  0.92  0.14  0.28  0.56  0.10  0.34  0.30  0.34  0.58  0.68
#> [2,]  0.88  0.40  0.94  0.84  0.94  0.82  0.38  0.66  0.36  0.76  0.80  0.96
#> [3,]  0.44  0.72  0.88  0.82  0.38  0.20  0.56  0.34  0.30  0.28  0.08  0.12
#> [4,]  0.56  0.36  0.38  0.72  0.42  0.14  0.62  0.74  0.02  0.02  0.78  0.14
#> [5,]  0.34  0.58  0.92  0.78  0.38  0.50  0.20  0.80  0.84  0.84  0.40  0.74
#> [6,]  0.92  0.24  0.36  0.58  0.78  0.86  0.66  0.42  0.42  0.90  0.88  0.92
#>      [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,]  0.82  0.22  0.74  0.58  0.32  0.46  0.70  0.88  0.80  0.18  0.82  0.32
#> [2,]  0.68  0.44  0.28  0.84  0.58  0.60  0.46  0.42  0.80  0.38  0.82  0.26
#> [3,]  0.40  0.20  0.54  0.30  0.18  0.84  0.70  0.58  0.58  0.60  0.60  0.14
#> [4,]  0.84  0.00  1.00  0.56  0.98  0.46  1.00  0.38  0.48  0.00  0.66  0.86
#> [5,]  0.96  0.46  0.50  0.38  0.04  0.34  0.02  0.98  0.64  0.12  0.30  0.54
#> [6,]  0.64  0.70  0.92  0.74  0.36  0.54  0.80  0.94  0.58  0.34  0.26  0.40