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私は WINBUGS を学ぼうとしており、テキストブックの例から調整された小さなモデル (以下のコード) を構築しようとしました。これは、成長率 (「R0」) と経時的な除去率（スクリーニングと治療）。ただし、さまざまなエラーメッセージが表示される傾向があります (「無効または予期しないトークンがスキャンされました」、「inits を実行できません」など)。したがって、より多くの WINBUGS の経験を持つ誰かが、私が WINBUGS の理解においてばかげた間違いを犯しているかどうかに注目して親切にすることができますか? 特に、母集団の逐次更新 (N.est[t+1] <- N.est[t] + newcases - obs) が WINBUGS で実行できるかどうかはわかりません。よろしくお願いします

set.seed(1)
n.years <- 10           # Number of years
N1 <- 30                # Initial population size
rnode <- 0.3
attendance <- 0.4

#generating some data. the model has a hidden population of infected carriers that grows, and is screened periodically (with x% attendance). screened individuals are removed from the population. the observed cases are used in the bayesian framework (BUGS) to infer the hidden parameters


obs <- N <- newcases <- numeric(n.years)
N[1] <- N1
for (t in 1:(n.years-1)){
  newcases[t] <- rbinom(n=1,size=N[t],prob=rnode) #
  obs[t] <- rbinom(1, N[t], attendance) #observed cases
  N[t+1] <- N[t] + newcases[t] - obs[t] }

# Specifying model in BUGS language
sink("ssm.bug")
cat("
    model { 
    # Priors and constraints
    N.est[1] ~ dbin(0.3, 100)    # Prior for initial population size
    rnode ~ dunif(0, 1)          # Prior for R0
    attendance ~ dunif(0, 1)     # Prior for attendance at screening

    # Likelihood
    # State process
    for (t in 1:(T-1)){
    newcases ~ dbin(rnode,N.est[t])
    obs ~ dbin(attendance,N.est[t])
    N.est[t+1] <- N.est[t] + newcases - obs    
    }
    ",fill = TRUE)
sink()

# Bundle data
bugs.data <- list(obs = obs, T = n.years)

# Initial values
inits <- function(){list(rnode=0.5,attendance=0.5, N.est = c(runif(1, 20, 40), rep(NA, (n.years-1))))} 

# Parameters monitored
parameters <- c("rnode", "attendance", "N.est")

# MCMC settings
ni <- 25000
nt <- 3
nb <- 10000
nc <- 3

# Call WinBUGS from R (BRT <1 min)
ssm <- bugs(bugs.data, inits, parameters, "ssm.bug", n.chains = nc, n.thin = nt, n.iter = ni, n.burnin = nb, debug = TRUE, bugs.directory = bugs.dir, working.directory = getwd())

# Define function to draw a graph to summarize results
graph.ssm <- function(ssm, N, y){
  fitted <- lower <- upper <- numeric()
  n.years <- length(y)
  for (i in 1:n.years){
    fitted[i] <- mean(ssm$sims.list$N.est[,i])
    lower[i] <- quantile(ssm$sims.list$N.est[,i], 0.025)
    upper[i] <- quantile(ssm$sims.list$N.est[,i], 0.975)}
  m1 <- min(c(y, fitted, N, lower))
  m2 <- max(c(y, fitted, N, upper))
  par(mar = c(4.5, 4, 1, 1), cex = 1.2)
  plot(0, 0, ylim = c(m1, m2), xlim = c(0.5, n.years), ylab = "Population size", xlab = "Year", las = 1, col = "black", type = "l", lwd = 2, frame = FALSE, axes = FALSE)
  axis(2, las = 1)
  axis(1, at = seq(0, n.years, 5), labels = seq(0, n.years, 5))
  axis(1, at = 0:n.years, labels = rep("", n.years + 1), tcl = -0.25)
  polygon(x = c(1:n.years, n.years:1), y = c(lower, upper[n.years:1]), col = "gray90", border = "gray90")
  points(N, type = "l", col = "red", lwd = 2)
  points(y, type = "l", col = "black", lwd = 2)
  points(fitted, type = "l", col = "blue", lwd = 2)
  legend(x = 1, y = m2, legend = c("True", "Observed", "Estimated"), lty = c(1, 1, 1), lwd = c(2, 2, 2), col = c("red", "black", "blue"), bty = "n", cex = 1)
}

# Execute function: Produce figure 
graph.ssm(ssm, N, y)
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