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 在全局环境中构建新函数(这篇文章的答案创建了允许用于相关结构的实现)。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:
#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:
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:
correlation = corhaversine(form=~lon+lat,mimic="corSpher")
在正常循环中。任何帮助将不胜感激!
解决方法
我不确定为什么您的 foreach
方法不起作用,而且我也不确定您实际计算的是什么。无论如何,您可以使用似乎有效的 parallel::parLapply()
尝试这种替代方法:
首先,我使用 rm(list=ls())
清除了工作区,然后我运行了 this answer 的整个第一个代码块,其中他们创建了 "corStruct"
类和 corHaversine
方法以将其作为以及下面的数据,准备好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))
})