使用时jags.parallel
,我收到以下错误:
> out <- jags.parallel(win.data, inits, params, "Poisson.OD.t.test.txt",
+ nc, ni, nb, nt);
Error in get(name, envir = envir) : invalid first argument
使用jags
函数的相同调用运行正常。我只找到了一个关于这个主题的线程,但只有一个推测性的建议在这里不适用也不行。
可重现的代码,取自生态学家 WinBUGS 简介,见第 14.1 章(稍作修改):
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
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 <- jags.parallel(win.data, inits, params, "Poisson.OD.t.test.txt",
nc, ni, nb, nt);
# THIS ALSO PRODUCES ERROR
out <- do.call(jags.parallel, list(win.data, inits, params, "Poisson.OD.t.test.txt",
nc, ni, nb, nt));