The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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))
}
thetas_true = rnorm(N,0,1)
tausd_true=0.5
taus_true = rnorm(N,0,tausd_true)
G_version = 3
phi_true = 0.8
lambdas_true <- c(-2, 1.6, .4, .055) # empirical from Wang 2017
Alphas <- 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 31
itempars_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))
#> 0
output_HMDCM_RT_sep
#>
#> Model: DINA_HO_RT_sep
#>
#> Sample Size: 350
#> Number of Items:
#> Number of Time Points:
#>
#> Chain Length: 100, burn-in: 50
summary(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: 0.6265
a <- 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,] 0.7695372
AAR_vec <- numeric(L)
for(t in 1:L){
AAR_vec[t] <- mean(Alphas[,,t]==a$Alphas_est[,,t])
}
AAR_vec
#> [1] 0.9221429 0.9435714 0.9592857 0.9685714 0.9614286
PAR_vec <- numeric(L)
for(t in 1:L){
PAR_vec[t] <- mean(rowSums((Alphas[,,t]-a$Alphas_est[,,t])^2)==0)
}
PAR_vec
#> [1] 0.7485714 0.8085714 0.8514286 0.8857143 0.8628571
a$DIC
#> Transition Response_Time Response Joint Total
#> D_bar 2205.921 135843.4 15042.20 3067.784 156159.4
#> D(theta_bar) 1905.363 135402.1 14870.05 3030.558 155208.1
#> DIC 2506.479 136284.8 15214.35 3105.011 157110.6
head(a$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 0.12
head(a$PPP_item_means)
#> [1] 0.50 0.38 0.56 0.46 0.42 0.32
head(a$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 0.68
library(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 setting
pp_check(output_HMDCM_RT_sep, type="total_latency")
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.