R：使用 mouseadds lm.cluster 进行聚类稳健标准误差 - 子集和权重的误差

我正在尝试使用 mouseadds 包中的 lm.cluster 函数来获取多重插补数据集的稳健聚类标准误差。

我能够运行它的标准版本，但当我尝试添加子集或权重时出现以下错误：

Error in eval(substitute(subset), data, env) : 
..1 used in an incorrect context, no ... to look in

无需子集或权重即可工作的示例：

require("mice")
require("miceadds")
data(data.ma01)
# imputation of the dataset: use six imputations
dat <- data.ma01[ , - c(1:2) ]
imp <- mice::mice( dat , maxit=3 , m=6 )
datlist <- miceadds::mids2datlist( imp )
# linear regression with cluster robust standard errors
mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,         
formula=read ~ paredu+ female ,  cluster = data.ma01$idschool )}  )

# extract parameters and covariance matrix
betas <- lapply( mod , FUN = function(rr){ coef(rr) } )
vars <- lapply( mod , FUN = function(rr){ vcov(rr) } )
# conduct statistical inference
summary(pool_mi( qhat = betas, u = vars ))

与子集中断的示例：

mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,         
formula=read ~ paredu+ female ,  cluster = data.ma01$idschool, subset=
(data.ma01$urban==1))}  )

Error during wrapup: ..1 used in an incorrect context, no ... to look in

打破重量的例子：

mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,         
formula=read ~ paredu+ female ,  cluster = data.ma01$idschool,
weights=data.ma01$studwgt)}  )

Error during wrapup: ..1 used in an incorrect context, no ... to look in

通过搜索，我认为在通过 lm 或 glm 包装器传递这些命令时，我遇到了与其他人类似的问题（例如：在函数内将参数传递给 R 中的 lm https://stackoverflow.com/questions/27261232/passing-argument-to-lm-in-r-within-function or R ：将参数传递给 R 函数内的 glm https://stackoverflow.com/questions/10858318/r-pass-argument-to-glm-inside-an-r-function or 将权重参数传递给 R 函数内的回归函数 https://stackoverflow.com/questions/27045340/passing-the-weights-argument-to-a-regression-function-inside-an-r-function)

但是，我不确定如何解决估算数据集和现有 lm.cluster 命令的问题。

Thanks

这适用于estimatr https://declaredesign.org/r/estimatrCRAN 上的包和estimatr::lm_robust()功能。两个注意事项：（1）您可以使用更改标准错误的类型se_type =(2) 我保留idschool在数据中，因为我们希望簇位于相同的位置data.frame当我们拟合模型时。

library(mice)
library(miceadds)
library(estimatr)

# imputation of the dataset: use six imputations
data(data.ma01)
dat <- data.ma01[, -c(1)] # note I keep idschool in data
imp <- mice::mice( dat , maxit = 3, m = 6)
datlist <- miceadds::mids2datlist(imp)

# linear regression with cluster robust standard errors
mod <- lapply(
  datlist, 
  function (dat) {
    estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool)
  }
)

# subset
mod <- lapply(
  datlist, 
  function (dat) {
    estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool, subset = urban == 1)
  }
)

# weights
mod <- lapply(
  datlist, 
  function (dat) {
    estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool, weights = studwgt)
  }
)

# note that you can use the `se_type` argument of lm_robust() 
# to change the vcov estimation

# extract parameters and covariance matrix
betas <- lapply(mod, coef)
vars <- lapply(mod, vcov)
# conduct statistical inference
summary(pool_mi( qhat = betas, u = vars ))