使用 data.table 识别高于固定效应回归的数据点

Question

我想识别回归线上方的数据点。我有一个面板数据集，我已经拟合了一个固定效应模型：

data <- data.frame(ID = c(1,1,1,1,2,2,3,3,3),
                   year = c(1,2,3,4,1,2,1,2,3),
                   progenyMean = c(90,78,92,69,86,73,82,85,91),
                   damMean = c(89,89,72,98,95,92,94,87,89)

ID, year, progenyMean, damMean
1, 1, 70, 69
1, 2, 68, 69
1, 3, 72, 72
1, 4, 69, 68
2, 1, 76, 75
2, 2, 73, 80
3, 1, 72, 74
3, 2, 75, 67
3, 3, 71, 69

# Fixed Effects Model in plm
fixed <- plm(progenyMean ~ damMean, data, model= "within", index = c("ID","year"))

我用固定效应回归线绘制了progenyMean vs damMean：

我想找出这条回归线上方的ID。 我已经使用以下代码计算了固定效应模型的预测值（基于此 post 的代码）

fitted <- as.numeric(fixed$model[[1]] - fixed$residuals)
> fitted
 [1]  71.24338  79.03766  74.86613  71.34263  70.83020  71.56797  72.17324  74.54755  71.16720  73.37487
[11]  70.58863  69.27203  71.05852  59.72911  63.43947  68.69871  67.25271  75.68397  76.30475  81.12128

是否可以使用上面的预测值和 R 中的 data.table 来识别固定效应回归线上方的 ID？

Answer 1

使用residuals 函数。正残差 = 线上方的点，负 = 线下方的点。

library(plm)
library(tidyverse)
library(ggplot2)

data <- data.frame(ID = c(1,1,1,1,2,2,3,3,3),
                   year = c(1,2,3,4,1,2,1,2,3),
                   progenyMean = c(90,78,92,69,86,73,82,85,91),
                   damMean = c(89,89,72,98,95,92,94,87,89))

fixed <- plm(progenyMean ~ damMean, data, model= "within", index = c("ID","year"))                   
residuals(fixed)

data %>% ggplot(aes(damMean, progenyMean)) +
  geom_point(data=data %>% filter(residuals(fixed)>0), col="red")+
  geom_point(data=data %>% filter(residuals(fixed)<0), col="blue")

data %>% mutate(
  test = ifelse(residuals(fixed)>0, "up", "down") %>% factor()
) %>% group_by(test) %>% summarise(
  n = n()
)