The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
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[@]dogado.de.
library(hmcdm)N = dim(Design_array)[1]
J = nrow(Q_matrix)
K = ncol(Q_matrix)
L = dim(Design_array)[3]tau <- numeric(K)
for(k in 1:K){
tau[k] <- runif(1,.2,.6)
}
R = matrix(0,K,K)
# Initial alphas
p_mastery <- c(.5,.5,.4,.4)
Alphas_0 <- matrix(0,N,K)
for(i in 1:N){
for(k in 1:K){
prereqs <- which(R[k,]==1)
if(length(prereqs)==0){
Alphas_0[i,k] <- rbinom(1,1,p_mastery[k])
}
if(length(prereqs)>0){
Alphas_0[i,k] <- prod(Alphas_0[i,prereqs])*rbinom(1,1,p_mastery)
}
}
}
Alphas <- sim_alphas(model="indept",taus=tau,N=N,L=L,R=R,alpha0=Alphas_0)
table(rowSums(Alphas[,,5]) - rowSums(Alphas[,,1])) # used to see how much transition has taken place
#>
#> 0 1 2 3 4
#> 22 95 125 81 27
Smats <- matrix(runif(J*K,.1,.3),c(J,K))
Gmats <- matrix(runif(J*K,.1,.3),c(J,K))
# Simulate rRUM parameters
r_stars <- Gmats / (1-Smats)
pi_stars <- apply((1-Smats)^Q_matrix, 1, prod)
Y_sim <- sim_hmcdm(model="rRUM",Alphas,Q_matrix,Design_array,
r_stars=r_stars,pi_stars=pi_stars)output_rRUM_indept = hmcdm(Y_sim,Q_matrix,"rRUM_indept",Design_array,
100,30,R = R)
#> 0
output_rRUM_indept
#>
#> Model: rRUM_indept
#>
#> Sample Size: 350
#> Number of Items:
#> Number of Time Points:
#>
#> Chain Length: 100, burn-in: 50
summary(output_rRUM_indept)
#>
#> Model: rRUM_indept
#>
#> Item Parameters:
#> r_stars1_EAP r_stars2_EAP r_stars3_EAP r_stars4_EAP pi_stars_EAP
#> 0.1993 0.51490 0.6957 0.6744 0.8504
#> 0.5519 0.43958 0.5587 0.6092 0.7719
#> 0.6596 0.51894 0.6409 0.3554 0.7504
#> 0.6738 0.68623 0.1715 0.6188 0.8434
#> 0.3924 0.09994 0.5141 0.6946 0.8155
#> ... 45 more items
#>
#> Transition Parameters:
#> taus_EAP
#> τ1 0.3851
#> τ2 0.6149
#> τ3 0.5582
#> τ4 0.3437
#>
#> Class Probabilities:
#> pis_EAP
#> 0000 0.11906
#> 0001 0.03655
#> 0010 0.09150
#> 0011 0.05177
#> 0100 0.07980
#> ... 11 more classes
#>
#> Deviance Information Criterion (DIC): 22933.92
#>
#> Posterior Predictive P-value (PPP):
#> M1: 0.5064
#> M2: 0.49
#> total scores: 0.6145
a <- summary(output_rRUM_indept)
head(a$r_stars_EAP)
#> [,1] [,2] [,3] [,4]
#> [1,] 0.1993376 0.51490431 0.6956522 0.6743655
#> [2,] 0.5519450 0.43958144 0.5586875 0.6092017
#> [3,] 0.6595621 0.51893944 0.6409174 0.3554333
#> [4,] 0.6738479 0.68622934 0.1714936 0.6188168
#> [5,] 0.3923977 0.09993945 0.5140721 0.6946310
#> [6,] 0.6193002 0.24105679 0.3053849 0.5611629(cor_pistars <- cor(as.vector(pi_stars),as.vector(a$pi_stars_EAP)))
#> [1] 0.9637738
(cor_rstars <- cor(as.vector(r_stars*Q_matrix),as.vector(a$r_stars_EAP*Q_matrix)))
#> [1] 0.9231634
AAR_vec <- numeric(L)
for(t in 1:L){
AAR_vec[t] <- mean(Alphas[,,t]==a$Alphas_est[,,t])
}
AAR_vec
#> [1] 0.8578571 0.8857143 0.9307143 0.9671429 0.9714286
PAR_vec <- numeric(L)
for(t in 1:L){
PAR_vec[t] <- mean(rowSums((Alphas[,,t]-a$Alphas_est[,,t])^2)==0)
}
PAR_vec
#> [1] 0.5285714 0.6314286 0.7628571 0.8800000 0.8942857a$DIC
#> Transition Response_Time Response Joint Total
#> D_bar 2064.491 NA 18249.38 1861.215 22175.09
#> D(theta_bar) 1981.126 NA 17570.21 1864.915 21416.26
#> DIC 2147.855 NA 18928.55 1857.516 22933.92
head(a$PPP_total_scores)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.02 0.38 0.12 0.82 0.62
#> [2,] 0.70 0.82 0.54 0.44 0.92
#> [3,] 0.78 0.26 0.68 0.54 0.96
#> [4,] 0.82 0.66 0.92 0.82 0.10
#> [5,] 0.78 0.58 0.92 0.02 0.80
#> [6,] 0.70 0.82 1.00 0.94 0.32
head(a$PPP_item_means)
#> [1] 0.44 0.52 0.50 0.58 0.40 0.50
head(a$PPP_item_ORs)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] NA 0.58 0.72 0.90 0.80 0.88 0.70 0.76 0.52 0.50 0.30 0.40 0.08 0.32
#> [2,] NA NA 0.16 0.12 0.82 0.34 0.46 0.96 0.40 0.54 0.64 0.88 0.12 0.76
#> [3,] NA NA NA 0.94 0.82 0.92 0.86 0.82 0.80 0.82 0.22 0.42 0.24 0.74
#> [4,] NA NA NA NA 0.64 0.96 0.76 0.82 0.40 0.74 0.10 0.86 0.22 0.52
#> [5,] NA NA NA NA NA 0.56 0.80 0.80 0.70 0.64 0.78 0.86 0.10 0.60
#> [6,] NA NA NA NA NA NA 0.76 1.00 0.86 0.40 0.02 0.22 0.22 0.28
#> [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
#> [1,] 0.40 0.78 0.38 0.08 0.90 0.64 0.46 0.70 0.14 0.44 0.32 0.64
#> [2,] 0.30 0.70 0.22 0.18 0.14 0.56 0.70 0.28 0.56 0.44 0.64 0.42
#> [3,] 0.10 0.54 0.52 0.40 0.36 0.76 0.38 0.72 0.10 0.04 0.34 0.22
#> [4,] 0.88 0.96 0.82 0.08 0.84 0.50 0.48 0.58 0.58 0.22 0.06 0.54
#> [5,] 0.64 0.10 0.88 0.08 0.50 0.88 0.82 0.88 0.12 0.74 0.54 0.86
#> [6,] 0.92 0.32 0.86 0.06 0.34 0.42 0.58 0.88 0.68 0.04 0.58 0.28
#> [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
#> [1,] 0.96 0.86 0.68 0.62 0.44 0.94 0.56 0.78 0.30 0.04 0.56 0.80
#> [2,] 0.16 0.68 0.50 0.50 0.66 0.40 0.44 0.42 0.78 0.98 0.68 0.62
#> [3,] 0.74 0.70 0.40 0.24 0.18 0.18 0.66 0.74 0.50 0.56 0.32 0.28
#> [4,] 0.40 0.08 0.36 0.46 0.14 0.62 0.20 0.88 0.14 0.50 0.06 0.32
#> [5,] 0.50 0.84 0.88 0.36 0.34 0.92 0.20 0.84 0.92 0.26 0.24 0.78
#> [6,] 0.70 0.32 0.02 0.88 0.00 0.60 0.04 0.40 0.46 0.18 0.36 0.38
#> [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
#> [1,] 0.72 0.26 1.00 1.00 0.26 0.70 1.00 0.28 0.68 0.86 0.96 0.92
#> [2,] 0.12 0.86 0.74 0.58 0.50 0.34 0.84 0.96 0.44 0.94 0.30 0.88
#> [3,] 0.46 0.10 0.66 0.30 0.04 0.34 0.50 0.42 0.66 0.04 1.00 0.14
#> [4,] 0.62 0.06 0.62 0.88 0.46 0.98 0.60 0.58 0.56 0.86 0.12 0.64
#> [5,] 0.58 0.80 0.74 0.86 0.38 0.50 0.60 0.78 0.30 0.58 0.80 0.88
#> [6,] 0.06 0.72 0.50 0.90 0.14 0.30 0.16 0.66 0.48 0.64 0.32 0.10These 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.
Health stats visible at Monitor.