missRanger基于R -ranger中的并行随机森林实现。因此，代码已经在所有内核上运行，像doParallel这样的东西只会让代码变得笨拙。
尝试通过missRanger的...参数将相关参数传递给ranger来加快计算速度，例如：

• num.trees = 20或
• max.depth = 8个

而是
免责声明：我是missRanger的作者。

这是多核概念的一个基本示例。这将突出基本概念，而不是着眼于时间问题。通过我的测试运行（对于更大数量的列），非并行版本更快。

library(doParallel)
library(missRanger)
library(data.table) #Needed for rbindlist at the end
cores=detectCores()
cl <- makeCluster(cores[1]) 
registerDoParallel(cl)
clusterEvalQ(cl, {library(missRanger)}) #Passing the package missRanger to all the cores
#Create some random columns
A=as.numeric(c(1,2,"",4,5,6,7,8,9,10,11,12,13,"",15,16,17,18,19,20))
B=as.numeric(c(120.5,128.1,126.5,122.5,127.1,129.7,124.2,123.7,"",122.3,120.9,122.4,125.7,"",128.2,129.1,121.2,128.4,127.6,125.1))
m = as.data.frame(matrix(0, ncol = 10, nrow = 20))
m[,1:5]=A
m[,6:10]=B
list_num=as.data.frame(seq(1,10,by=1)) #A sequence of column numbers for the different cores to run the function for

#Note that the optimal process would have been to take columns 1:3
#and run it on one core, 4:6 to run it on the 2nd core and so on.

#Function to run on the parallel cores

zzz=function(list_num){
  m_new=m[,list_num] #Note the function takes the column number as an argument
  m_new=missRanger(m_new[1:length(m_new)], pmm.k = 3, num.trees = 100)
}

clusterExport(cl=cl, list("m"),envir=environment()) #Export your list
zz=parLapply(cl=cl,fun=zzz,X=list_num) #Pass the function and the list of numbers here
zzzz=data.frame(rbindlist(zz)) #rbind the 
stopCluster(cl)

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mice 3.15.0和更高版本具有提供并行处理的新futuremice()函数。它可用于通过“rf”方法通过ranger::ranger()插补缺失数据。