r-如何从使用stargezer的plm-FE回归中获得介于和总体R2之间的值

Question

免责声明：这个问题与我两天前问的这个问题非常相关，但现在它与stargazer()输出中介于和总体R2之间的实现有关，而不是像以前那样在summary()中。

有没有办法让plm()为我计算R2和总体R2之间的值，并将它们包含在stargazer()输出中？

为了澄清我对R2之间、总体上和内部的理解，请参阅StackExchange上的答案。

我的理解是plm只在R2范围内计算。我正在运行模型内的Twoways效果。

library(plm)
library(stargazer)
# Create some random data
set.seed(1) 
x=rnorm(100); fe=rep(rnorm(10),each=10); id=rep(1:10,each=10); ti=rep(1:10,10); e=rnorm(100)
y=x+fe+e
data=data.frame(y,x,id,ti)
# Get plm within R2
reg=plm(y~x,model="within",index=c("id","ti"), effect = "twoways", data=data) 
stargazer(reg)

我现在还想在stargazer()输出中包含介于和之间的总R2。我该怎么做

为了明确我对介于和总体R2:之间的含义

# Pooled Version (overall R2)
reg1=lm(y~x)
summary(reg1)$r.squared
# Between R2
y.means=tapply(y,id,mean)[id]
x.means=tapply(x,id,mean)[id]
reg2=lm(y.means~x.means)
summary(reg2)$r.squared

Answer 1

要在stargazer中执行此操作，可以使用add.lines()参数然而，这会将行添加到摘要统计部分的开头，并且在不干扰源代码的情况下无法进行更改，这非常糟糕。我更喜欢huxtable，它提供了一种表构建语法，并且更具可扩展性和可定制性。

library(tidyverse)
library(plm)
library(huxtable)
# Create some random data
set.seed(1) 
x=rnorm(100); fe=rep(rnorm(10),each=10); id=rep(1:10,each=10); ti=rep(1:10,10); e=rnorm(100)
y=x+fe+e
data=data.frame(y,x,id,ti)
# Get plm within R2
reg=plm(y~x,model="within",index=c("id","ti"), effect = "twoways", data=data) 
stargazer(reg, type = "text", 
add.lines = list(c("Overall R2", round(r.squared(reg, model = "pooled"), 3)),
c("Between R2", round(r.squared(update(reg, effect = "individual", model = "between")), 3))))
#> 
#> ========================================
#>                  Dependent variable:    
#>              ---------------------------
#>                           y             
#> ----------------------------------------
#> x                     1.128***          
#>                        (0.113)          
#>                                         
#> ----------------------------------------
#> Overall R2              0.337           
#> Between R2              0.174           
#> Observations             100            
#> R2                      0.554           
#> Adjusted R2             0.448           
#> F Statistic    99.483*** (df = 1; 80)   
#> ========================================
#> Note:        *p<0.1; **p<0.05; ***p<0.01

# I prefer huxreg, which is much more customizable!
# Create a data frame of the R2 values
r2s <- tibble(
name = c("Overall R2", "Between R2"), 
value = c(r.squared(reg, model = "pooled"), 
r.squared(update(reg, effect = "individual", model = "between")))) 
tab <- huxreg(reg) %>% 
# Add new R2 values
add_rows(hux(r2s), after = 4)
# Rename R2 
tab[7, 1] <- "Within R2"
tab %>% huxtable::print_screen()
#> ─────────────────────────────────────────────────
#>                                    (1)           
#>                         ─────────────────────────
#>   x                                   1.128 ***  
#>                                      (0.113)     
#>                         ─────────────────────────
#>   N                                 100          
#>   Overall R2                          0.337      
#>   Between R2                          0.174      
#>   Within R2                           0.554      
#> ─────────────────────────────────────────────────
#>   *** p < 0.001; ** p < 0.01; * p < 0.05.        
#> 
#> Column names: names, model1

^{由reprex包(v0.3.0(于2020-04-08创建}