Stargazer不承认tobit，即使它是受支持的

我想让

stargazer

打印

AER:：tobit

回归（数据在底部）

require（AER）
要求（观星者）
s1.tobit使用
stargazer
v5.2.2和
AER
v1.2-9，只有将
AER:：
放在tobit前面，它才能工作，这很奇怪。也许你应该写一份bug报告，尽管我强烈建议使用
texreg
而不是
stargazer

require(AER)
require(stargazer)
m1.tobit <- AER::tobit(taxrate ~ votewon + industry + size + urbanisation + vote,
                       left=12, right=33, data=DF)
> stargazer(m1.tobit)

% Error: Unrecognized object type.

m2.tobit <- tobit(taxrate ~ votewon + industry + size + urbanisation + vote,
                  left=12, right=33, data=DF)
> stargazer(m2.tobit)

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Sat, Jan 16, 2021 - 20:13:07
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{1}{c}{\textit{Dependent variable:}} \\ 
\cline{2-2} 
\\[-1.8ex] & taxrate \\ 
\hline \\[-1.8ex] 
 votewon & $-$1.593 \\ 
  & (1.558) \\ 
  & \\ 
 industryE & $-$0.787 \\ 
  & (1.446) \\ 
  & \\ 
 industryF & $-$2.861$^{*}$ \\ 
  & (1.515) \\ 
  & \\ 
 size & $-$0.552 \\ 
  & (0.447) \\ 
  & \\ 
 urbanisationB & $-$1.703 \\ 
  & (1.525) \\ 
  & \\ 
 urbanisationC & $-$3.097$^{*}$ \\ 
  & (1.689) \\ 
  & \\ 
 vote & 4.176$^{***}$ \\ 
  & (1.615) \\ 
  & \\ 
 Constant & 25.257$^{***}$ \\ 
  & (1.934) \\ 
  & \\ 
\hline \\[-1.8ex] 
Observations & 100 \\ 
Log Likelihood & $-$299.532 \\ 
Wald Test & 13.061$^{*}$ (df = 7) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table} 

require（AER）
要求（观星者）
m1.托比特观星者（m1.托比特）
%错误：无法识别的对象类型。
m2.tobit stargazer（m2.tobit）
%表由哈佛大学Marek Hlavac的stargazer v.5.2.2创建。电子邮件：hlavac在fas.harvard.edu
%日期和时间：2021年1月16日星期六-20:13:07
\开始{table}[！htbp]\
\标题{}
\标签{}
\开始{tabular}{@{\extracolsep{5pt}}lc}
\\[-1.8ex]\hline
\hline\\[-1.8ex]
&\multicolumn{1}{c}{\textit{因变量：}}}\\
\跃层{2-2}
\\[-1.8ex]&税率\\
\hline\\[-1.8ex]
votewon&$-$1.593\\
& (1.558) \\ 
& \\ 
工业&$-$0.787\\
& (1.446) \\ 
& \\ 
行业&$-$2.861$^{*}$\\
& (1.515) \\ 
& \\ 
尺寸&$-$0.552\\
& (0.447) \\ 
& \\ 
城市化B&$-$1.703\\
& (1.525) \\ 
& \\ 
城市化&$-$3.097$^{*}$\\
& (1.689) \\ 
& \\ 
投票4.176美元^{***}$\\
& (1.615) \\ 
& \\ 
常数&25.257$^{***}$\\
& (1.934) \\ 
& \\ 
\hline\\[-1.8ex]
观察值&100\\
对数可能性&$-$299.532\\
瓦尔德测试&13.061$^{*}$（df=7）\\
\赫林
\hline\\[-1.8ex]
\textit{Note:}&\multicolumn{1}{r}{$^{*}$p$
DF <- structure(list(country = c("C", "C", "C", "C", "J", "J", "B", 
"B", "F", "F", "E", "E", "D", "D", "F", "F", "I", "I", "J", "J", 
"E", "E", "C", "C", "I", "I", "I", "I", "I", "I", "C", "C", "H", 
"H", "J", "J", "G", "G", "J", "J", "I", "I", "C", "C", "D", "D", 
"A", "A", "G", "G", "E", "E", "J", "J", "G", "G", "I", "I", "I", 
"I", "J", "J", "G", "G", "E", "E", "G", "G", "E", "E", "F", "F", 
"I", "I", "B", "B", "E", "E", "H", "H", "B", "B", "A", "A", "I", 
"I", "I", "I", "F", "F", "E", "E", "I", "I", "J", "J", "D", "D", 
"F", "F"), year = c(2005, 2010, 2010, 2005, 2005, 2010, 2010, 
2005, 2010, 2005, 2005, 2010, 2010, 2005, 2005, 2010, 2005, 2010, 
2005, 2010, 2010, 2005, 2010, 2005, 2005, 2010, 2005, 2010, 2010, 
2005, 2010, 2005, 2005, 2010, 2010, 2005, 2005, 2010, 2005, 2010, 
2005, 2010, 2005, 2010, 2010, 2005, 2005, 2010, 2010, 2005, 2010, 
2005, 2010, 2005, 2010, 2005, 2010, 2005, 2010, 2005, 2010, 2005, 
2010, 2005, 2010, 2005, 2010, 2005, 2005, 2010, 2005, 2010, 2005, 
2010, 2005, 2010, 2005, 2010, 2005, 2010, 2010, 2005, 2005, 2010, 
2005, 2010, 2010, 2005, 2010, 2005, 2010, 2005, 2005, 2010, 2005, 
2010, 2010, 2005, 2010, 2005), sales = c(15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9, 15.48, 12.39, 3.72, 
23.61, 4, 31.87, 25.33, 7.64, -0.26, 2.9), industry = c("D", 
"D", "E", "E", "F", "F", "F", "F", "D", "D", "E", "E", "D", "D", 
"E", "E", "F", "F", "F", "F", "D", "D", "F", "F", "E", "E", "D", 
"D", "D", "D", "E", "E", "F", "F", "D", "D", "E", "E", "E", "E", 
"D", "D", "E", "E", "D", "D", "D", "D", "E", "E", "D", "D", "F", 
"F", "D", "D", "D", "D", "E", "E", "D", "D", "E", "E", "D", "D", 
"D", "D", "D", "D", "F", "F", "F", "F", "E", "E", "D", "D", "E", 
"E", "F", "F", "E", "E", "F", "F", "E", "E", "F", "F", "D", "D", 
"D", "D", "D", "D", "D", "D", "F", "F"), urbanisation = c("B", 
"B", "A", "A", "B", "B", "A", "A", "C", "C", "C", "C", "A", "A", 
"B", "B", "C", "C", "A", "A", "C", "C", "B", "B", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "C", "C", "B", "B", "B", "B", 
"B", "B", "C", "C", "A", "A", "B", "B", "B", "B", "A", "A", "B", 
"B", "A", "A", "A", "A", "B", "B", "C", "C", "A", "A", "C", "C", 
"A", "A", "B", "B", "A", "A", "B", "B", "B", "B", "B", "B", "C", 
"C", "A", "A", "A", "A", "A", "A", "A", "A", "C", "C", "A", "A", 
"B", "B", "A", "A", "B", "B", "B", "B"), size = c(1, 1, 5, 5, 
5, 5, 1, 1, 1, 1, 5, 5, 5, 5, 2, 2, 2, 2, 5, 5, 1, 1, 1, 1, 5, 
5, 5, 5, 4, 4, 5, 5, 5, 5, 4, 4, 2, 2, 5, 5, 1, 1, 1, 1, 2, 2, 
1, 1, 2, 2, 5, 5, 1, 1, 3, 3, 2, 2, 2, 2, 5, 5, 4, 4, 1, 1, 5, 
5, 2, 2, 5, 5, 2, 2, 2, 2, 4, 4, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 
5, 5, 3, 3, 2, 2, 3, 3, 1, 1, 5, 5), base_rate = c(14L, 14L, 
14L, 14L, 19L, 19L, 30L, 30L, 20L, 20L, 29L, 29L, 20L, 20L, 20L, 
20L, 24L, 24L, 19L, 19L, 29L, 29L, 14L, 14L, 24L, 24L, 24L, 24L, 
24L, 24L, 14L, 14L, 17L, 17L, 19L, 19L, 33L, 33L, 19L, 19L, 24L, 
24L, 14L, 14L, 20L, 20L, 23L, 23L, 33L, 33L, 29L, 29L, 19L, 19L, 
33L, 33L, 24L, 24L, 24L, 24L, 19L, 19L, 33L, 33L, 29L, 29L, 33L, 
33L, 29L, 29L, 20L, 20L, 24L, 24L, 30L, 30L, 29L, 29L, 17L, 17L, 
30L, 30L, 23L, 23L, 24L, 24L, 24L, 24L, 20L, 20L, 29L, 29L, 24L, 
24L, 19L, 19L, 20L, 20L, 20L, 20L), taxrate = c(12L, 14L, 14L, 
12L, 21L, 18L, 30L, 30L, 20L, 20L, 29L, 30L, 20L, 20L, 20L, 20L, 
24L, 24L, 21L, 18L, 30L, 29L, 14L, 12L, 24L, 24L, 24L, 24L, 24L, 
24L, 14L, 12L, 18L, 19L, 18L, 21L, 33L, 32L, 21L, 18L, 24L, 24L, 
12L, 14L, 20L, 20L, 22L, 25L, 32L, 33L, 30L, 29L, 18L, 21L, 32L, 
33L, 24L, 24L, 24L, 24L, 18L, 21L, 32L, 33L, 30L, 29L, 32L, 33L, 
29L, 30L, 20L, 20L, 24L, 24L, 30L, 30L, 29L, 30L, 18L, 19L, 30L, 
30L, 22L, 25L, 24L, 24L, 24L, 24L, 20L, 20L, 30L, 29L, 24L, 24L, 
21L, 18L, 20L, 20L, 20L, 20L), vote = c(0, 0, 0, 0, 1, 1, 1, 
0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 
1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 
1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 
1, 0, 1, 1, 1, 1, 0, 1, 1), votewon = c(0, 0, 0, 0, 1, 0, 1, 
0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 
1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 
0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 
1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 
0, 0, 1, 1, 0, 1, 0, 1, 1)), class = "data.frame", row.names = c(NA, 
-100L))

## convert variables to factors beforehand
DF[c(1, 2, 4, 5, 6, 9, 10)] <- lapply(DF[c(1, 2, 4, 5, 6, 9, 10)], factor)

require(AER)
require(stargazer)
m1.tobit <- AER::tobit(taxrate ~ votewon + industry + size + urbanisation + vote,
                       left=12, right=33, data=DF)
> stargazer(m1.tobit)

% Error: Unrecognized object type.

m2.tobit <- tobit(taxrate ~ votewon + industry + size + urbanisation + vote,
                  left=12, right=33, data=DF)
> stargazer(m2.tobit)

% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
% Date and time: Sat, Jan 16, 2021 - 20:13:07
\begin{table}[!htbp] \centering 
  \caption{} 
  \label{} 
\begin{tabular}{@{\extracolsep{5pt}}lc} 
\\[-1.8ex]\hline 
\hline \\[-1.8ex] 
 & \multicolumn{1}{c}{\textit{Dependent variable:}} \\ 
\cline{2-2} 
\\[-1.8ex] & taxrate \\ 
\hline \\[-1.8ex] 
 votewon & $-$1.593 \\ 
  & (1.558) \\ 
  & \\ 
 industryE & $-$0.787 \\ 
  & (1.446) \\ 
  & \\ 
 industryF & $-$2.861$^{*}$ \\ 
  & (1.515) \\ 
  & \\ 
 size & $-$0.552 \\ 
  & (0.447) \\ 
  & \\ 
 urbanisationB & $-$1.703 \\ 
  & (1.525) \\ 
  & \\ 
 urbanisationC & $-$3.097$^{*}$ \\ 
  & (1.689) \\ 
  & \\ 
 vote & 4.176$^{***}$ \\ 
  & (1.615) \\ 
  & \\ 
 Constant & 25.257$^{***}$ \\ 
  & (1.934) \\ 
  & \\ 
\hline \\[-1.8ex] 
Observations & 100 \\ 
Log Likelihood & $-$299.532 \\ 
Wald Test & 13.061$^{*}$ (df = 7) \\ 
\hline 
\hline \\[-1.8ex] 
\textit{Note:}  & \multicolumn{1}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
\end{tabular} 
\end{table}