我正在运行4个回归模型，具有不同的规格，但每个模型都将 `x` 作为回归器。我正在使用`estimatr`包中的标准错误类型和来自`estimatr`包的聚类。
然后我使用`texreg`来获得一个格式良好的回归表，这是我的可重现示例：
```R
library(estimatr)
library(texreg)
# 加载数据
data <- data.frame(
  x = rnorm(100),
  z = rnorm(100),
  b = rbinom(100, 1, 0.5),
  n = rpois(100, 10)
)
data$y <- 0.5 + 2 * data$x + 0.1 * data$z + 0.5 * data$b - 0.1 * (data$x - 0.5) ^ 2 + rnorm(100)
# 定义模型
model1 <- lm_robust(y ~ x, data = data, clusters = data$b, se_type = "stata")
model2 <- lm_robust(y ~ x + z, data = data, clusters = data$b, se_type = "stata")
model3 <- lm_robust(y ~ x + b, data = data, clusters = data$z, se_type = "stata")
model4 <- lm_robust(y ~ x + z + b, data = data, clusters = data$n, se_type = "stata")
# 使用screenreg创建表格
texreg(
  list(model1, model2, model3, model4), table = FALSE,
  custom.model.names = c("Model 1", "Model 2", "Model 3", "Model 4"),
  custom.coef.map =  list("x" = "only coeff"),
  include.ci = FALSE, include.adjr = FALSE, include.rsquared = FALSE, inlcude.rmse = FALSE,
  include.nobs = TRUE,
  digits = 3
)

\begin{tabular}{l c c c c}
\hline
 & Model 1 & Model 2 & Model 3 & Model 4 \\
\hline
only coeff & $2.394^{*}$ & $2.397^{*}$ & $2.379^{***}$ & $2.382^{***}$ \\
           & $(0.095)$   & $(0.092)$   & $(0.100)$     & $(0.083)$     \\
\hline
R$^2$      & $0.840$     & $0.843$     & $0.855$       & $0.861$       \\
Adj. R$^2$ & $0.838$     & $0.840$     & $0.852$       & $0.857$       \\
Statistic  & $634.115$   & $$          & $282.093$     & $440.925$     \\
P Value    & $0.025$     & $$          & $0.000$       & $0.000$       \\
DF Resid.  & $1.000$     & $1.000$     & $99.000$      & $14.000$      \\
nobs       & $100$       & $100$       & $100$         & $100$         \\
\hline
\multicolumn{5}{l}{\scriptsize{$^{***}p<0.001$; $^{**}p<0.01$; $^{*}p<0.05$}}
\end{tabular}

警告消息：
1: 在 data.frame(gof.names = colnames(out), gof = as.numeric(out)，中引入了 NAs。
2: 在 doTryCatch(return(expr), name, parentenv, handler) 中：
  texreg 用 broom 包提取了以下 GOF 度量，但无法将其转换为数字类型：se_type
3: 在 data.frame(gof.names = colnames(out)，gof = as.numeric(out)，中引入了 NAs。
4: 在 doTryCatch(return(expr), name, parentenv, handler) 中：
  texreg 用 broom 包提取了以下 GOF 度量，但无法将其转换为数字类型：Statistic
5: 在 data.frame(gof.names = colnames(out), gof = as.numeric(out)，中引入了 NAs。
6: 在 doTryCatch(return(expr), name, parentenv, handler) 中：
  texreg 用 broom 包提取了以下 GOF 度量，但无法将其转换为数字类型：se_type
7: 在 data.frame(gof.names = colnames(out), gof = as.numeric(out)，中引入了 NAs。
8: 在 doTryCatch(return(expr), name, parentenv, handler) 中：
  texreg 用 broom 包提取了以下 GOF 度量，但无法将其转换为数字类型：se_type

\begin{tabular}{l c c c c}
\hline
 & Model 1 & Model 2 & Model 3 & Model 4 \\
\hline
only coeff & $2.394^{*}$ & $2.397^{*}$ & $2.379^{***}$ & $2.382^{***}$ \\
           & $(0.095)$   & $(0.092)$   & $(0.100)$     & $(0.083)$     \\
\hline
nobs       & $100$       & $100$       & $100$         & $100$         \\
\hline
\multicolumn{5}{l}{\scriptsize{$^{***}p<0.001$; $^{**}p<0.01$; $^{*}p<0.05$}}
\end{tabular}

<details>
<summary>英文:</summary>
I am running 4 regressions, with different specifications, but every model has `x` as a regressor. I am using the standard errors stata type and clusters from the `estimatr` package
Then I am using `texreg` for getting a nicely formatted regression table, here is my reproducible example:

As you can see in the `include.x` part I am only including the `n.obs`. I just want that. But it seems the code ignores that part, because this is my result.

I think it has to do with the `lm_robust` function (because it works with just `lm`), but I really need the `lm_robust` because of standard errors (I need them to be identical to the obtained in stata)
Furthermore I get this warnings:

My desired result would be:

How can I accomplish this?
Thanks in advance !
</details>
# 答案1
**得分**: 1
`stargazer`是我用来进行标准误差调整的首选包。以下是创建您想要的表格的示例代码，包括Stata风格的集群标准误差：
```R
library(lmtest)
library(sandwich)
library(stargazer)
set.seed(44)
# 加载数据
data <- data.frame(
  x = rnorm(100),
  z = rnorm(100),
  b = rbinom(100, 1, 0.5),
  n = rpois(100, 10)
)
data$y <- 0.5 + 2 * data$x + 0.1 * data$z + 0.5 * data$b - 0.1 * (data$x - 0.5) ^ 2 + rnorm(100)
# 定义模型
model1 <- lm(y ~ x,     data = data)
model2 <- lm(y ~ x + z, data = data)
model3 <- lm(y ~ x + b, data = data)
model4 <- lm(y ~ x + z + b, data = data)
# 制作集群标准误差
se1 = as.vector(coeftest(model1,vcov = vcovCL,cluster = ~b, type="HC1")[,"Std. Error"])
se2 = as.vector(coeftest(model2,vcov = vcovCL,cluster = ~b,  type="HC1")[,"Std. Error"])
se3 = as.vector(coeftest(model3,vcov = vcovCL,cluster = ~z,  type="HC1")[,"Std. Error"])
se4 = as.vector(coeftest(model4,vcov = vcovCL,cluster = ~n,  type="HC1")[,"Std. Error"])
stargazer(model1,model2,model3,model4,type="latex",
          se=list(se1,se2,se3,se4), omit=c("Constant","z","b"),
          omit.stat = c("f","ser","adj.rsq","rsq"))

================================================
                     Dependent variable:        
             -----------------------------------
                              y                 
               (1)      (2)      (3)      (4)   
------------------------------------------------
x            2.071*** 2.079*** 2.123*** 2.135***
             (0.103)  (0.124)  (0.149)  (0.111) 
                                                
------------------------------------------------
Observations   100      100      100      100   
================================================
Note:                *p<0.1; **p<0.05; ***p<0.01

         (0.023)     (0.006)     (0.101)      (0.148)