[過去ﾛｸﾞ] 高校数学の質問スレ（医者・東大卒専用） Part438 (1002ﾚｽ)
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816: 05/01(木)09:09 ID:L1qIlz9/(2/4) AAS
Stanで作ったらコンパイルに時間がかかる。簡単なモデルはJAGSの方がいい。離散変数も扱えるし。
# JAGS model
library(rjags)

# Prepare the data
outcome_data <- c(rep(1, 17), rep(2, 21), rep(3, 15), rep(4, 21), rep(5, 20), rep(6, 6))
N <- length(outcome_data)
data_jags <- list(outcome = outcome_data, N = N)

# Initial values (adjust as needed)
inits_jags <- list(
list(alpha = rep(1, 6), eta = 1),
list(alpha = runif(6, 0.1, 2), eta = 5)
)

# Compile the model
model_jags <- jags.model(
file = "hierarchical_dice_model.jag",
data = data_jags,
n.chains = 2,
n.adapt = 1000
)

# Sampling
samples_jags <- coda.samples(
model = model_jags,
variable.names = c("prob_simplex", "alpha", "eta"),
n.iter = 4000
)

# Summary of the results
cat("\nJAGS Sampling Results Summary:\n")
summary(samples_jags)

# Extract posterior samples (prob_simplex)
prob_simplex_posterior_jags <- as.matrix(samples_jags[, grep("prob_simplex", varnames(samples_jags))])
head(prob_simplex_posterior_jags)

# Plotting (example: posterior distribution of probabilities for each outcome)
cat("\nPosterior Distribution Plots for Each Outcome:\n")
par(mfrow = c(2, 3))
for (i in 1:6) {
plot(prob_simplex_posterior_jags[, i], type = "l", main = paste("Prob[", i, "]"), xlab = "Iteration", ylab = "Probability")
abline(h = 1/6, col = "red", lty = 2)
}

dice_prob_mean=prob_simplex_posterior_jags
colors <- c("skyblue", "lightcoral", "lightgreen", "gold", "lightsalmon", "lightcyan")
for (i in 1:ncol(dice_prob_mean)) {
BEST::plotPost(dice_prob_mean[, i],
compVal=1/6,
xlab=paste("pip ", i),
xlim=c(0, 0.4),
main="",
col=colors[i],
border="black")
}

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