R Tobit回归时的奇异性误差

我试图估计一个标准的托比特模型，它被删失为零

变量是

因变量：幸福感

自变量：

• 城市（芝加哥，纽约）
• 性别（男，女）
• 就业（0=失业，1=就业）
• 工作类型（失业、蓝色、白色）
• 假期（失业，每周1天，每周2天）

“工作类型”和“假日”变量与“就业”变量相互作用

我正在使用

censReg

包进行tobit回归

censReg(Happiness ~ City + Gender + Employment:Worktype + Employment:Holiday)

但是

summary（）

返回以下错误

Error in printCoefmat(coef(x, logSigma = logSigma), digits = digits) : 
  'x' must be coefficient matrix/data frame

为了找出原因，我运行了OLS回归

censReg(Happiness ~ City + Gender + Employment:Worktype + Employment:Holiday)

有一些NA值，我认为这是因为模型设计和变量设置（某些变量似乎存在奇点。而

'Employment'=0

的人有值

'Worktype'=unemployee

，

'Holidays'=unemployee

。这可能是原因？）

我怎么能忽略NA值并运行tobit回归而不出错呢

下面是可复制的代码

Happiness <- c(0, 80, 39, 0, 69, 90, 100, 30)

 City <- as.factor(c("New York", "Chicago", "Chicago", "New York", "Chicago", 
"Chicago", "New York", "New York"))
 Gender <- as.factor(c(0, 1, 0, 1, 1, 1, 0, 1)) # 0 = man, 1 = woman.
 Employment <- c(0,1, 0, 0, 1 ,1 , 1 , 1) # 0 = unemployed, 1 = employed.
 Worktype <- as.factor(c(0, 2, 0, 0, 1, 1, 2,2))
 levels(Worktype) <- c("Unemployed", "Bluecolor", "Whitecolor")
 Holiday <- as.factor(c(0, 1, 0, 0, 2, 2, 2, 1))
 levels(Holiday) <- c("Unemployed", "1 day a week", "2 day a week")

 data <- data.frame(Happiness, City, Gender, Employment, Worktype, Holiday)
 reg <- lm(Happiness ~ City + Gender + Employment:Worktype +      
           Employment:Holiday)
 summary(reg)

 install.packages("censReg")
 library(censReg)
 tobitreg <- censReg(Happiness ~ City + Gender + Employment:Worktype +      
                     Employment:Holiday)
 summary(tobitreg)

---

Happiness如果您一步一步地调试对censReg的调用，您将实现以下maxLik优化：

result <- maxLik(censRegLogLikCross, start = start, 
      yVec = yVec, xMat = xMat, left = left, right = right, 
      obsBelow = obsBelow, obsBetween = obsBetween, obsAbove = obsAbove, 
      ...)

summary
函数获取此
NULL
，它解释了您得到的最终错误消息

要覆盖此选项，可以设置
start
参数：

tobitreg <- censReg(formula = Happiness ~ City + Gender + Employment:Worktype +      
                      Employment:Holiday, start = rep(0,9) )
summary(tobitreg)

Call:
censReg(formula = Happiness ~ City + Gender + Employment:Worktype + 
    Employment:Holiday, start = rep(0, 9))

Observations:
         Total  Left-censored     Uncensored Right-censored 
             8              2              6              0 

Coefficients:
                               Estimate Std. error t value Pr(> t)
(Intercept)                      38.666        Inf       0       1
CityNew York                    -50.669        Inf       0       1
Gender1                        -360.633        Inf       0       1
Employment:WorktypeUnemployed     0.000        Inf       0       1
Employment:WorktypeBluecolor    345.674        Inf       0       1
Employment:WorktypeWhitecolor    56.210        Inf       0       1
Employment:Holiday1 day a week  346.091        Inf       0       1
Employment:Holiday2 day a week   55.793        Inf       0       1
logSigma                          1.794        Inf       0       1

Newton-Raphson maximisation, 141 iterations
Return code 1: gradient close to zero
Log-likelihood: -19.35431 on 9 Df

我很快就会查出来的。非常感谢您的努力！
tobitreg <- censReg(formula = Happiness ~ City + Gender + Employment:Worktype +      
                      Employment:Holiday, start = rep(0,9) )
summary(tobitreg)

Call:
censReg(formula = Happiness ~ City + Gender + Employment:Worktype + 
    Employment:Holiday, start = rep(0, 9))

Observations:
         Total  Left-censored     Uncensored Right-censored 
             8              2              6              0 

Coefficients:
                               Estimate Std. error t value Pr(> t)
(Intercept)                      38.666        Inf       0       1
CityNew York                    -50.669        Inf       0       1
Gender1                        -360.633        Inf       0       1
Employment:WorktypeUnemployed     0.000        Inf       0       1
Employment:WorktypeBluecolor    345.674        Inf       0       1
Employment:WorktypeWhitecolor    56.210        Inf       0       1
Employment:Holiday1 day a week  346.091        Inf       0       1
Employment:Holiday2 day a week   55.793        Inf       0       1
logSigma                          1.794        Inf       0       1

Newton-Raphson maximisation, 141 iterations
Return code 1: gradient close to zero
Log-likelihood: -19.35431 on 9 Df

tobitreg <- censReg(formula = Happiness ~ City + Gender + Employment )
summary(tobitreg)
Call:
censReg(formula = Happiness ~ City + Gender + Employment)

Observations:
         Total  Left-censored     Uncensored Right-censored 
             8              2              6              0 

Coefficients:
             Estimate Std. error t value  Pr(> t)    
(Intercept)   38.6141     5.7188   6.752 1.46e-11 ***
CityNew York -50.1813     6.4885  -7.734 1.04e-14 ***
Gender1      -70.3859     8.2943  -8.486  < 2e-16 ***
Employment   111.5672    10.0927  11.054  < 2e-16 ***
logSigma       1.7930     0.2837   6.320 2.61e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Newton-Raphson maximisation, 8 iterations
Return code 1: gradient close to zero
Log-likelihood: -19.36113 on 5 Df