Trung tâm cá nhân
sách gpt4 ai đã đi

r - 进行 Tobit 回归时的奇异性错误

In lại 作者：行者123 更新时间：2023-12-03 16:52:33 26 4
mua khóa gpt4 Nike

我正在尝试估计一个标准的 tobit 模型，该模型被审查为零。

变量是

因变量 : 幸福

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)

    • “Worktype”和“Holiday”变量与“Employment”变量相互作用。

    Tôi đang sử dụng censReg tobit回归包。 
    censReg(Happiness ~ City + Gender + Employment:Worktype + Employment:Holiday)

    Nhưng 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。

    有一些 NA 值，我认为这是因为模型设计和变量设置(某些变量似乎有奇点。而具有 'Employment' = 0 的人具有值 'Worktype' = Unemployed , 'Holidays' = Unemployed 。这可能是原因？ ) 
    lm(Happiness ~ City + Gender + Employment:Worktype + Employment:Holiday)


    Coefficients: (2 not defined because of singularities)
                                   Estimate Std. Error t value Pr(>|t|)  
    (Intercept) 41.750 9.697 4.305 0.0499 *
    CityNew York -44.500 11.197 -3.974 0.0579 .
    Gender1 2.750 14.812 0.186 0.8698  
    Employment:WorktypeUnemployed NA NA NA NA  
    Employment:WorktypeBluecolor 35.000 17.704 1.977 0.1867  
    Employment:WorktypeWhitecolor 102.750 14.812 6.937 0.0202 *
    Employment:Holiday1 day a week -70.000 22.394 -3.126 0.0889 .
    Employment:Holiday2 day a week NA NA NA NA

    我怎样才能忽略 NA 值并无错误地运行 tobit 回归？

    下面是可重现的代码。 
    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)

    1 Câu trả lời

    如果您逐步调试对 censReg 的调用，则会达到以下 maxLik 优化:

    result <- maxLik(censRegLogLikCross, start = start, 
          yVec = yVec, xMat = xMat, left = left, right = right, 
          obsBelow = obsBelow, obsBetween = obsBetween, obsAbove = obsAbove, 
          ...)
    初始条件向量 bắt đầu这是使用 OLS 回归确定的包含 NA正如您已经发现的那样，对于两个 coefs:
  • 就业:工作类型失业
  • 就业:每周放假2天

    • 这会导致 maxLik返回NULL，并带有错误消息: 
    Return code 100: Initial value out of range.
    summary函数得到这个 VÔ GIÁ TRỊ这解释了您得到的最终错误消息。
    要覆盖它，您可以设置 bắt đầu范围 : 
    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
    即使错误消息消失了，结果也不可靠:
  • 错误 = 信息
  • 梯度接近 0 :没有最优值，解决方案是超平面

    • 回归中的 NA 系数表明这些系数与其他系数线性相关，因此您需要删除其中的一些以获得唯一解。
    正如您所怀疑的，这是因为您只有 Employement = 0khi worktype = Unemployed ，因此模型无法估计 Employment:WorktypeUnemployed 的系数.与 Employment:Holiday 相同的问题系数。
    所以我担心您正在评估的回归模型没有单一的最佳解决方案。
    如果你摆脱了链接的变量，这有效: 
    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

    关于r - 进行 Tobit 回归时的奇异性错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47988969/

    26 4 0
    Bài viết được đề xuất: visual-studio-2017 - 无法在 VS 2017 中使用 NuGet 包管理器添加 jquery
    Bài viết được đề xuất: r - 我们可以为rmarkdown中yaml header 中的biblio-style参数设置哪些选择？
    Bài viết được đề xuất: nativescript - 错误 ENOENT : no such file or directory, scandir 'PATH_PORJECT\platforms\android\app\src\main\res'
    Bài viết được đề xuất: Azure AD B2C - 使用图形 API 填充 "Authentication Email"
    行者123
    Hồ sơ cá nhân

    Tôi là một lập trình viên xuất sắc, rất giỏi!

    Nhận phiếu giảm giá Didi Taxi miễn phí
    Mã giảm giá Didi Taxi
    Các bài viết nóng hổi trên toàn bộ trang web
    Giấy chứng nhận ICP Bắc Kinh số 000000
    Hợp tác quảng cáo: 1813099741@qq.com 6ren.com