如何将对象导出到R中函数内的并行集群

Question

我正在编写一个函数来组合和组织数据,然后使用基数R中的并行函数并行运行MCMC链.我的函数如下.

dm100zip <- function(y, n.burn = 1, n.it = 3000, n.thin = 1) {
  y <- array(c(as.matrix(y[,2:9]), as.matrix(y[ ,10:17])), c(length(y$Plot), 8, 2))
  nplots <- nrow(y)
  ncap1 <- apply(y[,1:8, 1],1,sum)
  ncap2 <- apply(y[,1:8, 2],1,sum)
  ncap <- as.matrix(cbind(ncap1, ncap2))
  ymax1 <- apply(y[,1:8, 1],1,sum)
  ymax2 <- apply(y[,1:8, 2],1,sum)

  # Bundle data for JAGS/BUGS
  jdata100 <- list(y=y, nplots=nplots, ncap=ncap)

  # Set initial values for Gibbs sampler
  inits100 <- function(){
    list(p0=runif(1, 1.1, 2),
      p.precip=runif(1, 0, 0.1),
      p.day = runif(1, -.5, 0.1))
  }

  # Set parameters of interest to monitor and save
  params100 <- c("N", "p0")

  # Run JAGS in parallel for improved speed
  CL <- makeCluster(3) # set number of clusters = to number of desired chains
  clusterExport(cl=CL, list("jdata100", "params100", "inits100", "ymax1", "ymax2", "n.burn", "jag", "n.thin")) # make data available to jags in diff cores
  clusterSetRNGStream(cl = CL, iseed = 5312)

  out <- clusterEvalQ(CL, {
    library(rjags)
    load.module('glm')
    jm <- jags.model("dm100zip.txt", jdata100, inits100, n.adapt = n.burn, n.chains = 1)
    fm <- coda.samples(jm, params100, n.iter = n.it, thin = n.thin)
    return(as.mcmc(fm))

  })

  out.list <- mcmc.list(out) # group output from each core into one list
  stopCluster(CL)

  return(out.list)
}

当我运行该函数时,我得到一个错误,即找不到n.burn,n.it和n.thin在clusterExport函数中使用.例如,

dm100zip.list.nain <- dm100zip(NAIN, n.burn = 1, n.it = 3000, n.thin = 1) # returns error

如果我在运行函数之前为每个值设置了值,那么它会使用这些值并运行正常.例如,

n.burn = 1
n.it = 1000
n.thin = 1
dm100zip.list.nain <- dm100zip(NAIN, n.burn = 1, n.it = 3000, n.thin = 1) 

这运行正常,但使用n.it = 1000而不是3000

有人可以帮助解释为什么ClusterExport函数使用全局环境中的对象而不是由在其中ClusterExport运行的函数指定的值吗？有没有解决的办法？

Answer 1

默认情况下,clusterExport在全局环境中查找"varlist"指定的变量.在您的情况下,它应该在dm100zip函数的本地环境中查找.要使它成功,请使用clusterExport"envir"参数:

clusterExport(cl=CL, list("jdata100", "params100", "inits100", "ymax1",
                          "ymax2", "n.burn", "jag", "n.thin"),
              envir=environment())

请注意,还将找到在全局环境中定义的"varlist"中的变量,但dm100zip中定义的值将优先.