5.12 MCMC for DINA model in Nimble R package

Code

library(nimble)
library(GDINA)
library(MCMCvis)

library(nimble, warn.conflicts = FALSE)

DINA.mcmc <- nimbleCode({
    for (n in 1:N) {
        for (j in 1:J) {
            prob[n, j] <- g[j] + (1 - s[j] - g[j]) * 1 * (sum(alpha[n,
                1:K] * Q[j, 1:K]) >= sum(Q[j, 1:K]))
            score[n, j] ~ dbern(prob[n, j])
        }
        latent.group.index[n] ~ dcat(pi[1:C])
        alpha[n, 1:K] <- all.patterns[latent.group.index[n], 1:K]
    }
    pi[1:C] ~ ddirch(delta[1:C])

    for (j in 1:J) {
        s[j] ~ dbeta(1, 1)
        g[j] ~ T(dbeta(1, 1), , 1 - s[j])

    }
})

score <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
J <- ncol(score)
K <- ncol(Q)
N <- nrow(score)
all.patterns <- GDINA::attributepattern(K)
C <- nrow(all.patterns)
DINAconstants <- list(Q = Q, N = N, J = J, K = K, C = C, delta = rep(1,
    C))
DINAdata <- list(score = score, all.patterns = as.matrix(all.patterns))

initials <- list(list(s = rep(0.2, J), g = rep(0.2, J), pi = rep(1/C, C),
    latent.group.index = sample(1:C, N, replace = TRUE)), list(s = rep(0.1,
    J), g = rep(0.1, J), pi = rep(1/C, C), latent.group.index = sample(1:C,
    N, replace = TRUE)))

nimbleMCMC_samples <- nimbleMCMC(code = DINA.mcmc, constants = DINAconstants,
    data = DINAdata, inits = initials, monitors = c("s", "g"), nburnin = 2500,
    niter = 5000, nchains = 2, setSeed = c(123, 456))

Code

library(MCMCvis)
MCMCvis::MCMCsummary(nimbleMCMC_samples, params = c("s", "g"), Rhat = TRUE,
    round = 2)

##       mean   sd 2.5%  50% 97.5% Rhat n.eff
## s[1]  0.26 0.03 0.20 0.26  0.31  1.0   306
## s[2]  0.28 0.03 0.23 0.28  0.33  1.0   408
## s[3]  0.11 0.02 0.07 0.11  0.16  1.0   308
## s[4]  0.20 0.03 0.14 0.20  0.25  1.0   334
## s[5]  0.29 0.04 0.22 0.29  0.36  1.0   370
## s[6]  0.07 0.02 0.04 0.07  0.11  1.0   493
## s[7]  0.32 0.03 0.26 0.32  0.37  1.0   597
## s[8]  0.31 0.04 0.23 0.31  0.37  1.0   248
## s[9]  0.21 0.03 0.16 0.21  0.26  1.0   665
## s[10] 0.16 0.03 0.10 0.16  0.22  1.0   585
## g[1]  0.07 0.05 0.00 0.07  0.17  1.0    66
## g[2]  0.09 0.03 0.04 0.09  0.15  1.0   220
## g[3]  0.07 0.03 0.03 0.07  0.13  1.0   192
## g[4]  0.22 0.02 0.18 0.22  0.26  1.0   517
## g[5]  0.08 0.01 0.06 0.08  0.11  1.0   809
## g[6]  0.59 0.03 0.53 0.59  0.63  1.0   533
## g[7]  0.26 0.02 0.21 0.26  0.30  1.0   683
## g[8]  0.15 0.02 0.11 0.15  0.19  1.0   449
## g[9]  0.27 0.02 0.23 0.27  0.31  1.0   723
## g[10] 0.33 0.02 0.30 0.33  0.37  1.0   690

Code

MCMCvis::MCMCtrace(nimbleMCMC_samples, params = c("g[1]", "s[1]"), ISB = FALSE,
    exact = TRUE, ind = TRUE, pdf = FALSE, Rhat = TRUE, n.eff = TRUE)