如何在vglm tobit模型中使用健壯的se和cluster se？

Question

我正在嘗試將一個tobit模型從Stata遷移到R.

強大的Stata命令只是向模型添加,vce(robust) 。 對於聚類，它將是,vce(cluster idvar) 。

可重復的Stata示例：

use http://www.ats.ucla.edu/stat/stata/dae/tobit, clear
tobit apt read math i.prog, ul(800)
tobit apt read math i.prog, ul(800) vce(cluster prog)

可重復的R例子：

library("VGAM")

dat <- read.csv("http://www.ats.ucla.edu/stat/data/tobit.csv")

summary(m <- vglm(apt ~ read + math + prog, tobit(Upper = 800), data = dat))

我的理解是coeftest(m, vcov = sandwich)應該給我強大的se。

但我得到以下內容： Error: $ operator not defined for this S4 class.

有人可以提出一種方法來估計vglm模型中的魯棒性se，還有用vglm聚類的方法嗎？

Answer 1

在花了一整天自己研究這個問題后，我想我終於找到了一個合適的方案： Zelig 。

http://docs.zeligproject.org/en/latest/zelig-tobit.html

比較沒有群集到群集：

WITHOUT

> summary(m <- zelig(apt ~ read + math + prog,
          below=0, above=Inf, model="tobit", data = dat))


 How to cite this model in Zelig:
  Kosuke Imai, Gary King, and Olivia Lau. 2015.
  "tobit: Linear regression for Left-Censored Dependent Variable"
  in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"
  http://gking.harvard.edu/zelig


Call:
"survreg"(formula = formula, dist = "gaussian", data = data, 
    robust = robust)
                Value Std. Error     z        p
(Intercept)    242.74     29.760  8.16 3.45e-16
read             2.55      0.576  4.43 9.24e-06
math             5.38      0.651  8.27 1.31e-16
proggeneral    -13.74     11.596 -1.18 2.36e-01
progvocational -48.83     12.818 -3.81 1.39e-04
Log(scale)       4.12      0.050 82.41 0.00e+00

Scale= 61.6 

Gaussian distribution
Loglik(model)= -1107.9   Loglik(intercept only)= -1202.8
    Chisq= 189.72 on 4 degrees of freedom, p= 0 
Number of Newton-Raphson Iterations: 5 
n= 200 

WITH

> summary(m <- zelig(apt ~ read + math + prog, below=0,
          above=Inf, model="tobit",
          data = dat,robust=T,cluster="prog"))


 How to cite this model in Zelig:
  Kosuke Imai, Gary King, and Olivia Lau. 2015.
  "tobit: Linear regression for Left-Censored Dependent Variable"
  in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software,"
  http://gking.harvard.edu/zelig


Call:
"survreg"(formula = formula, dist = "gaussian", data = data, 
    robust = robust)
                Value Std. Err (Naive SE)       z        p
(Intercept)    242.74   2.8315     29.760   85.73 0.00e+00
read             2.55   0.3159      0.576    8.08 6.40e-16
math             5.38   0.2770      0.651   19.44 3.78e-84
proggeneral    -13.74   0.3252     11.596  -42.25 0.00e+00
progvocational -48.83   0.1978     12.818 -246.83 0.00e+00
Log(scale)       4.12   0.0586      0.050   70.34 0.00e+00

Scale= 61.6 

Gaussian distribution
Loglik(model)= -1107.9   Loglik(intercept only)= -1202.8
    Chisq= 189.72 on 4 degrees of freedom, p= 0 
(Loglikelihood assumes independent observations)
Number of Newton-Raphson Iterations: 5 
n= 200