问题描述
我正在尝试运行具有特定空间相关结构的 gls 模型,该结构来自修改 nlme 包/从此 post(这篇文章的答案创建了允许实现相关性的新函数结构体).不幸的是,当我通过 foreach 循环运行它时,我无法让这个空间相关结构起作用:
I am trying to run gls models with a specific spatial correlation structure that comes from modifying the nlme package/ building new functions in the global environment from this post (the answer from this post that creates new functions which allows for the implementation of the correlation structure). Unfortunately I cannot get this spatial correlation structure to work when I run this through a foreach loop:
#setup example data
data("mtcars")
mtcars$lon = runif(nrow(mtcars)) #include lon and lat for the new correlation structure
mtcars$lat = runif(nrow(mtcars))
mtcars$marker = c(rep(1, nrow(mtcars)/2), rep(2, nrow(mtcars)/2)) #values for iterations
#set up cluster
detectCores()
cl <- parallel::makeCluster(6, setup_strategy = "sequential")
doParallel::registerDoParallel(cl)
#run model
list_models<-foreach(i=1:2, .packages=c('nlme'), .combine = cbind,
.export=ls(.GlobalEnv)) %dopar% {
.GlobalEnv$i <- i
model_trial<-gls(disp ~ wt,
correlation = corHaversine(form=~lon+lat,
mimic="corSpher"),
data = mtcars)
}
stopCluster(cl)
当我运行它时,我收到错误消息:
When I run this I get the error message:
Error in { :
task 1 failed - "do not know how to calculate correlation matrix of "corHaversine" object"
In addition: Warning message:
In e$fun(obj, substitute(ex), parent.frame(), e$data) :
already exporting variable(s): corHaversine, mtcars, path_df1
该模型在添加相关结构的情况下运行良好:
The model works fine with the added correlation structure :
correlation = corHaversine(form=~lon+lat,mimic="corSpher")
在正常循环中.任何帮助将不胜感激!
in a normal loop. Any help would be appreciated!
推荐答案
我不知道为什么你的 foreach
方法不起作用,而且我也不确定你实际上是什么计算.无论如何,您可以使用 parallel::parLapply()
尝试这种替代方法,它似乎有效:
I'm not sure why your foreach
approach doesn't work, andd I'm also not sure what you're actually calculating. Anyway, you may try this alternative approach using parallel::parLapply()
which seems to work:
首先,我使用 rm(list=ls())
清除了工作区,然后我运行了 这个答案,他们在其中创建了 "corStruct"
类和 corHaversine
方法,以便在工作区和 Data 中使用它> 下面,准备clusterExport()
.
First, I cleared workspace using rm(list=ls())
, then I ran the entire first codeblock of this answer where they create "corStruct"
class and corHaversine
method to have it in workspace as well as the Data below, ready for clusterExport()
.
library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterEvalQ(cl, library(nlme))
clusterExport(cl, ls())
r <- parLapply(cl=cl, X=1:2, fun=function(i) {
gls(disp ~ wt,
correlation=corHaversine(form= ~ lon + lat, mimic="corSpher"),
data=mtcars)
})
stopCluster(cl) ## stop cluster
r ## result
# [[1]]
# Generalized least squares fit by REML
# Model: disp ~ wt
# Data: mtcars
# Log-restricted-likelihood: -166.6083
#
# Coefficients:
# (Intercept) wt
# -122.4464 110.9652
#
# Correlation Structure: corHaversine
# Formula: ~lon + lat
# Parameter estimate(s):
# range
# 10.24478
# Degrees of freedom: 32 total; 30 residual
# Residual standard error: 58.19052
#
# [[2]]
# Generalized least squares fit by REML
# Model: disp ~ wt
# Data: mtcars
# Log-restricted-likelihood: -166.6083
#
# Coefficients:
# (Intercept) wt
# -122.4464 110.9652
#
# Correlation Structure: corHaversine
# Formula: ~lon + lat
# Parameter estimate(s):
# range
# 10.24478
# Degrees of freedom: 32 total; 30 residual
# Residual standard error: 58.19052
数据:
set.seed(42) ## for sake of reproducibility
mtcars <- within(mtcars, {
lon <- runif(nrow(mtcars))
lat <- runif(nrow(mtcars))
marker <- c(rep(1, nrow(mtcars)/2), rep(2, nrow(mtcars)/2))
})
这篇关于如何将自定义函数加载到 R 中的 foreach 循环中?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!