我正在R中建立一个患者水平模拟模型。它需要随着时间的推移（使用两个内部循环）为每个患者（接受和不接受治疗）生成两个数据帧。然后，我需要为模型中需要的每个患者循环内循环。然后，将内部循环的结果存储在全局环境的列表中。
要尝试加快处理速度，我想使用foreach包并行运行外部循环。使用%do%时，循环按预期工作（不并行运行循环）。但是，将其设置为%dopar%以并行运行后，内部循环将不再导出到全局环境中的列表中，并且我收到错误消息：
{中的错误：任务1失败-“未找到对象'Patient_Data'”
我在下面提供了代码，其中提供了我的外循环函数的%do%和%dopar%版本的有效示例。内部循环已从示例中删除，并仅替换了简单的概率绘制。
任何帮助将不胜感激。
library(tidyverse)
library(foreach)
library(doSNOW)
# Input
rm(list = ls())
Patient_Number <- 1000
#### Create a place to store patient data generated during the simulation ####
Patient_Data <- vector("list", length = Patient_Number)
#### Function - Non-parallel ####
Run_Sim <- function(){
cl <- makeCluster(4, type = "SOCK")
registerDoSNOW(cl)
# record the time the model started
model_start <- Sys.time()
print(noquote(paste("Time model started: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
#### Simulate Patient's BCVA scores ####
# create progress bar
print(noquote("Simulating Patients:"))
pb <- txtProgressBar(min = 0, max = Patient_Number, style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
foreach(i = 1:Patient_Number, .packages = c("tidyverse"), .inorder = FALSE,
.export = ls(globalenv()),
.options.snow = opts) %do% {
This_Patient <- list(
Patient_ID = 0,
Intervention = 0,
Comparator = 0
)
This_Patient_Draw_Int <- rnorm(1, mean = 50, sd = 7.8) # These normally would be more complex functions generating a data frame for each patient
This_Patient_Draw_Comp <- rnorm(1, mean = 44, sd = 10) # These normally would be more complex functions generating a data frame for each patient
This_Patient$Patient_ID <- i
This_Patient$Intervention <- This_Patient_Draw_Int
This_Patient$Comparator <- This_Patient_Draw_Comp
Patient_Data[[i]] <<- This_Patient
}
# stop the progress bar
close(pb)
# record when model finished
model_finish <- Sys.time()
print(noquote(paste("Time model finished: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
print(noquote(paste("Model took ", round(difftime(model_finish, model_start, units = c("mins")), 0),
" minute(s) to simulate ", Patient_Number, " Patients", sep = "")))
stopCluster(cl)
}
Run_Sim()
#### Parallel version using foreach %dopar% ####
rm(list = ls())
Patient_Number <- 1000
Patient_Data <- vector("list", length = Patient_Number)
Run_Sim_Para <- function(){
cl <- makeCluster(4, type = "SOCK")
registerDoSNOW(cl)
# record the time the model started
model_start <- Sys.time()
print(noquote(paste("Time model started: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
#### Simulate Patient's BCVA scores ####
# create progress bar
print(noquote("Simulating Patients:"))
pb <- txtProgressBar(min = 0, max = Patient_Number, style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
foreach(i = 1:Patient_Number, .packages = c("tidyverse"), .inorder = FALSE,
.export = ls(globalenv()),
.options.snow = opts) %dopar% {
This_Patient <- list(
Patient_ID = 0,
Intervention = 0,
Comparator = 0
)
This_Patient_Draw_Int <- rnorm(1, mean = 50, sd = 7.8) # These normally would be more complex functions generating a data frame for each patient
This_Patient_Draw_Comp <- rnorm(1, mean = 44, sd = 10) # These normally would be more complex functions generating a data frame for each patient
This_Patient$Patient_ID <- i
This_Patient$Intervention <- This_Patient_Draw_Int
This_Patient$Comparator <- This_Patient_Draw_Comp
Patient_Data[[i]] <<- This_Patient
}
# stop the progress bar
close(pb)
# record when model finished
model_finish <- Sys.time()
print(noquote(paste("Time model finished: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
print(noquote(paste("Model took ", round(difftime(model_finish, model_start, units = c("mins")), 0),
" minute(s) to simulate ", Patient_Number, " Patients", sep = "")))
stopCluster(cl)
}
Run_Sim_Para()
答案 0 :(得分：0)
我已通过以下操作解决了该问题；
foreach函数assign函数用于将foreach循环的输出传递给全局环境中称为“ Patient_Data”的对象，而不是使用可变状态来更新全局环境中已存在的列表。环境下面的示例代码。希望这对其他可能遇到类似问题的人有所帮助。
library(tidyverse)
library(foreach)
library(doSNOW)
# Input
rm(list = ls())
Patient_Number <- 1e4
#### Create a listing function which will be ran through "foreach" ####
list_func <- function(Patient_ID_Code){
This_Patient <- list(
Patient_ID = 0,
Intervention = 0,
Comparator = 0
)
This_Patient_Draw_Int <- rnorm(1, mean = 50, sd = 7.8) # These normally would be more complex functions generating a data frame for each patient
This_Patient_Draw_Comp <- rnorm(1, mean = 44, sd = 10) # These normally would be more complex functions generating a data frame for each patient
This_Patient$Patient_ID <- Patient_ID_Code
This_Patient$Intervention <- This_Patient_Draw_Int
This_Patient$Comparator <- This_Patient_Draw_Comp
return(This_Patient)
}
Run_Sim_Para <- function(){
cl <- parallel::makeCluster(parallel::detectCores() - 1)
registerDoSNOW(cl)
# record the time the model started
model_start <- Sys.time()
print(noquote(paste("Time model started: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
#### Simulate Patient's BCVA scores ####
# create progress bar
print(noquote("Simulating Patients:"))
pb <- txtProgressBar(min = 0, max = Patient_Number, style = 3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress = progress)
test <- foreach(i = 1:Patient_Number, .packages = c("tidyverse"),
.export = ls(.GlobalEnv),
.options.snow = opts) %dopar% {
list_func(i)
}
# stop the progress bar
close(pb)
# record when model finished
model_finish <- Sys.time()
print(noquote(paste("Time model finished: ", format(Sys.time(), "%a %d %b %Y %X"), sep = "")))
print(noquote(paste("Model took ", round(difftime(model_finish, model_start, units = c("mins")), 0),
" minute(s) to simulate ", Patient_Number, " Patients", sep = "")))
stopCluster(cl)
assign("Patient_Data", test, envir = .GlobalEnv)
}
Run_Sim_Para()