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我只有 2 个核心 CPU，所以逻辑上我只想为jags.parallel. 不幸的是，当我尝试它并且链数为 3 或 4 时，jags 失败并出现错误：

res[[ch]] 中的错误：下标越界

是否不允许使用更少的线程数（比链数）？

我在文档中没有遇到过这样的声明。无论如何，当您的 CPU 只有 2 个内核时，在 4 个线程/集群中运行 4 个链是没有意义的！线程将争夺 CPU，缓存不会得到最佳使用，结果将比仅使用 2 个线程慢得多。

完整代码：

set.seed(123)

### 14.1.2. Data generation
n.site <- 10
x <- gl(n = 2, k = n.site, labels = c("grassland", "arable"))
eps <- rnorm(2*n.site, mean = 0, sd = 0.5)# Normal random effect
lambda.OD <- exp(0.69 +(0.92*(as.numeric(x)-1) + eps) )
lambda.Poisson <- exp(0.69 +(0.92*(as.numeric(x)-1)) ) # For comparison

C.OD <- rpois(n = 2*n.site, lambda = lambda.OD)
C.Poisson <- rpois(n = 2*n.site, lambda = lambda.Poisson)

### 14.1.4. Analysis using WinBUGS
# Define model
sink("Poisson.OD.t.test.txt")
cat("
model {
# Priors
 alpha ~ dnorm(0,0.001)
 beta ~ dnorm(0,0.001)
 sigma ~ dunif(0, 10)   
 tau <- 1 / (sigma * sigma)
 maybe_overdisp <- mean(exp_eps[])

# Likelihood
 for (i in 1:n) {
    C.OD[i] ~ dpois(lambda[i]) 
    log(lambda[i]) <- alpha + beta *x[i] #+ eps[i]
    eps[i] ~ dnorm(0, tau)
    exp_eps[i] <- exp(eps[i])
 }
}
",fill=TRUE)
sink()


# Bundle data
x = as.numeric(x)-1
n = length(x)
win.data <- list(C.OD = C.OD, x = as.numeric(x)-1, n = length(x))

# Inits function
inits <- function(){ list(alpha=rlnorm(1), beta=rlnorm(1), sigma = rlnorm(1))}

# Parameters to estimate
params <- c("lambda","alpha", "beta", "sigma", "maybe_overdisp")

# MCMC settings
nc <- 3     # Number of chains
ni <- 3000     # Number of draws from posterior per chain
nb <- 1000     # Number of draws to discard as burn-in
nt <- 5     # Thinning rate

require(R2jags)

# THIS WORKS FINE
out <- R2jags::jags(win.data, inits, params, "Poisson.OD.t.test.txt",
    nc, ni, nb, nt);

# THIS PRODUCES ERROR
out <- do.call(jags.parallel, list(names(win.data), inits, params, "Poisson.OD.t.test.txt",
    nc, ni, nb, nt, n.cluster = 2));
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