r语言 - expand.model.frame within lapply() &Map():环境与fixest::feo

Question

在估计lm和fixest::feols()模型后，我试图在lapply()中使用expand.model.frame()。

当它发生时，我不断地遇到fixest::feols错误，可能是因为我对环境感到困惑。

下面是一个典型的例子:

library(fixest) 
library(lfe)
library(estimatr)
fun <- function(object, clustid){

fml <- object$call$formula
if(!inherits(clustid, "formula")){
clustid <- reformulate(clustid)
}

expand.model.frame(
model = object, 
extras = clustid, 
#envir = environment(terms(object)),
#envir = .GlobalEnv,
na.expand = FALSE
)

}
data(mtcars)
tmp_lm <- lapply(list(mtcars, mtcars), function(x){lm(mpg ~ qsec ,data = x)})
lapply(tmp_lm, function(x) fun(x, clustid = "carb"))
tmp_feols <- lapply(list(mtcars, mtcars), function(x){feols(mpg ~ qsec ,data = x)})
lapply(tmp_feols, function(x) fun(x, clustid = ~carb))
# Error in eval(model$call$data, envir) : object 'x' not found
tmp_felm <- lapply(list(mtcars, mtcars), function(x){felm(mpg ~ qsec ,data = x)})
lapply(tmp_felm, function(x) fun(x, clustid = ~carb))
tmp_estimatr <- lapply(list(mtcars, mtcars), function(x){lm_robust(mpg ~ qsec ,data = x)})
lapply(tmp_estimatr, function(x) fun(x, clustid = ~carb))

有没有人有一个建议，我将如何调整fun()，使我不会再遇到下面的错误?

Answer 1

我可以从您的注释代码中看到，您一直在试验envir参数。你的思路是对的。"fixest"类包含一个名为call_env的对象，以确保您可以访问正确的评估环境。这意味着您需要根据传递的对象类型修改代码以获得正确的调用环境:

fun <- function(object, clustid){
if(!inherits(clustid, "formula")){
clustid <- reformulate(clustid)
}

e <- if(class(object) == "fixest") 
object$call_env else environment(formula(object))

expand.model.frame(
model = object, 
extras = clustid, 
envir = e,
na.expand = FALSE
)

}

这允许:

data(mtcars)
tmp_feols <- lapply(list(mtcars, mtcars), function(x){feols(mpg ~ qsec ,data = x)})
lapply(tmp_feols, function(x) fun(x, clustid = ~carb))
#> [[1]]
#>                      mpg  qsec carb
#> Mazda RX4           21.0 16.46    4
#> Mazda RX4 Wag       21.0 17.02    4
#> Datsun 710          22.8 18.61    1
#> Hornet 4 Drive      21.4 19.44    1
#> Hornet Sportabout   18.7 17.02    2
#> Valiant             18.1 20.22    1
#> Duster 360          14.3 15.84    4
#> Merc 240D           24.4 20.00    2
#> Merc 230            22.8 22.90    2
#> Merc 280            19.2 18.30    4
#> Merc 280C           17.8 18.90    4
#> Merc 450SE          16.4 17.40    3
#> Merc 450SL          17.3 17.60    3
#> Merc 450SLC         15.2 18.00    3
#> Cadillac Fleetwood  10.4 17.98    4
#> Lincoln Continental 10.4 17.82    4
#> Chrysler Imperial   14.7 17.42    4
#> Fiat 128            32.4 19.47    1
#> Honda Civic         30.4 18.52    2
#> Toyota Corolla      33.9 19.90    1
#> Toyota Corona       21.5 20.01    1
#> Dodge Challenger    15.5 16.87    2
#> AMC Javelin         15.2 17.30    2
#> Camaro Z28          13.3 15.41    4
#> Pontiac Firebird    19.2 17.05    2
#> Fiat X1-9           27.3 18.90    1
#> Porsche 914-2       26.0 16.70    2
#> Lotus Europa        30.4 16.90    2
#> Ford Pantera L      15.8 14.50    4
#> Ferrari Dino        19.7 15.50    6
#> Maserati Bora       15.0 14.60    8
#> Volvo 142E          21.4 18.60    2
#> 
#> [[2]]
#>                      mpg  qsec carb
#> Mazda RX4           21.0 16.46    4
#> Mazda RX4 Wag       21.0 17.02    4
#> Datsun 710          22.8 18.61    1
#> Hornet 4 Drive      21.4 19.44    1
#> Hornet Sportabout   18.7 17.02    2
#> Valiant             18.1 20.22    1
#> Duster 360          14.3 15.84    4
#> Merc 240D           24.4 20.00    2
#> Merc 230            22.8 22.90    2
#> Merc 280            19.2 18.30    4
#> Merc 280C           17.8 18.90    4
#> Merc 450SE          16.4 17.40    3
#> Merc 450SL          17.3 17.60    3
#> Merc 450SLC         15.2 18.00    3
#> Cadillac Fleetwood  10.4 17.98    4
#> Lincoln Continental 10.4 17.82    4
#> Chrysler Imperial   14.7 17.42    4
#> Fiat 128            32.4 19.47    1
#> Honda Civic         30.4 18.52    2
#> Toyota Corolla      33.9 19.90    1
#> Toyota Corona       21.5 20.01    1
#> Dodge Challenger    15.5 16.87    2
#> AMC Javelin         15.2 17.30    2
#> Camaro Z28          13.3 15.41    4
#> Pontiac Firebird    19.2 17.05    2
#> Fiat X1-9           27.3 18.90    1
#> Porsche 914-2       26.0 16.70    2
#> Lotus Europa        30.4 16.90    2
#> Ford Pantera L      15.8 14.50    4
#> Ferrari Dino        19.7 15.50    6
#> Maserati Bora       15.0 14.60    8
#> Volvo 142E          21.4 18.60    2

但仍将在lm版本上工作。

当然，更复杂的方法是创建一个新的泛型函数，根据类进行分派，而不是在一个大函数中处理类。

^{在2022-05-28由reprex包(v2.0.1)创建}