模型的聚集标准误答案

【问题标题】：Clustered standard errors for a model模型的聚集标准误
【发布时间】：2021-09-16 03:12:45
【问题描述】：

我想计算以下回归模型的clustered standard errors。不知道怎么定义？我不知道如何翻译公式并将其应用于 R？

我的回归模型：

model3<- lm(Dishwash_dur ~ work_minutes + 
              work_minutes_squared +  
              relevel(employment, ref = "FT")+
              work_minutes*relevel(employment, ref = "FT")+ 
              dishwas_betw + 
              DVAge + 
              DMSex+ 
              DVHsize+ 
              Income+
              NumChild+
              dishwas_betw * work_minutes+
              dishwas_betw * work_minutes_squared,
            data = df, weights=dishwas_timedep)




Sample data:

输入(df[1:3,])

df<-structure(list(serial = c(11011209, 11011209, 11011210), pnum = c(1, 
2, 2), `diary day` = c(4, 4, 5), work_minutes = c(450, 450, 480
), work_hours = c(8, 8, 0), work_minutes_squared = c(230400, 
230400, 0), cooking_betw = c(0.159090909, 0.371212121, 0.271428571
), dishwas_betw = c(NA, 0.28030303, 0.271428571), houseclean_betw = c(NA_real_, 
NA_real_, NA_real_), laundry_betw = c(NA_real_, NA_real_, NA_real_
), ironing_betw = c(NA_real_, NA_real_, NA_real_), tv1_betw = c(0.227272727, 
0.28030303, 0.088095238), tv2_betw = c(NA_real_, NA_real_, NA_real_
), tv3_betw = c(NA_real_, NA_real_, NA_real_), tv4_betw = c(0.242424242, 
0.242424242, NA), tv5_betw = c(NA_real_, NA_real_, NA_real_), 
    tv6_betw = c(NA, NA, NA), tv7_betw = c(NA_real_, NA_real_, 
    NA_real_), employment = structure(c(1L, 2L, 2L), .Label = c("FT", 
    "PT"), class = "factor"), cooking_timedep = c(2.773774781, 
    2.773774781, 3.417989523), dishwas_timedep = c(1.99418589, 
    1.99418589, 2.625000906), houseclean_timedep = c(5.327815177, 
    5.327815177, 4.750929283), laundry_timedep = c(0.964285364, 
    0.964285364, 3.126314747), ironing_timedep = c(2.559321793, 
    2.559321793, 2.292682967), TV_timedep = c(5.086074482, 5.086074482, 
    5.012631788), TV_dur = c(70, 240, 60), Dishwash_dur = c(0, 
    20, 10), Foodprep_dur = c(80, 150, 10), Cleaningdwelling_dur = c(0, 
    0, 10), Ironing_dur = c(0, 0, 0), Laundry_dur = c(0, 0, 0
    ), WorkType = c("RE", "RE", "RE"), DMSex = c(1, 2, 1), DVAge = c(69, 
    60, 36), DVHsize = c(2, 2, 4), Income = c(1500, 1500, 3500
    ), NumChild = c(0, 0, 2)), row.names = c(NA, -3L), class = c("tbl_df", 
"tbl", "data.frame"))

【问题讨论】：

标签： r regression linear-regression logistic-regression

【解决方案1】：

在lmtest::coeftest 中使用集群稳健协方差矩阵sandwich::vcovCL。您的数据不完整，因此我以 R 中内置的 iris 数据为例。

fit <- lm(Sepal.Length ~ ., iris)

library(lmtest);library(sandwich)
ct <- coeftest(fit, vcov.=vcovCL(fit, cluster= ~ Species))
ct
# t test of coefficients:
#   
#                    Estimate Std. Error t value  Pr(>|t|)    
# (Intercept)        2.171266   0.314159  6.9114 1.438e-10 ***
# Sepal.Width        0.495889   0.121453  4.0830 7.344e-05 ***
# Petal.Length       0.829244   0.097682  8.4892 2.325e-14 ***
# Petal.Width       -0.315155   0.110369 -2.8555  0.004933 ** 
# Speciesversicolor -0.723562   0.383713 -1.8857  0.061351 .  
# Speciesvirginica  -1.023498   0.528100 -1.9381  0.054571 .  
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

如果由于某种原因您只需要标准错误，您可以这样做：

se <- ct[, 2]
se
# (Intercept)       Sepal.Width      Petal.Length       Petal.Width Speciesversicolor  Speciesvirginica 
#  0.31415923        0.12145252        0.09768204        0.11036863        0.38371255        0.52809998

【讨论】：