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我最近收到了一个关于这里的答案的问题，关于如何在使用 Stargazer 输出回归结果时更改变量的名称。答案不适用于rmslrm的功能。具体来说，系数输出正确，但标准误差消失了。这是一个可重现的示例：

library(rms)
library(stargazer)
stargazer(attitude)

# logit models
m1 <- lrm(rating ~ complaints + learning + privileges,x=TRUE, y=TRUE,data=attitude)
m2 <- lrm(rating ~ complaints + learning + privileges,x=TRUE, y=TRUE,data=attitude)
names(m1$coefficients)[names(m1$coefficients) == "privileges"] <- "past"
names(m2$coefficients)[names(m2$coefficients) == "privileges"] <- "past"

stargazer(m1,m2, type="text")

知道如何进行这项工作吗？

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1 回答 1

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答案是lrm类对象不仅存储系数，还存储方差-协方差矩阵，因此您需要执行额外的步骤：

rownames(m1$var)[rownames(m1$var) == "privileges"] <- "past"
rownames(m2$var)[rownames(m2$var) == "privileges"] <- "past"
colnames(m1$var)[colnames(m1$var) == "privileges"] <- "past"
colnames(m2$var)[colnames(m2$var) == "privileges"] <- "past"

这会改变方差-协方差矩阵的行名和列名，从而产生正确的结果：

stargazer(m1,m2, type="text")

==========================================
                  Dependent variable:     
              ----------------------------
                         rating           
                   (1)            (2)     
------------------------------------------

....

complaints       0.196***      0.196***   
                 (0.045)        (0.045)   

learning          0.063          0.063    
                 (0.039)        (0.039)   

past              -0.034        -0.034    
                 (0.035)        (0.035)   

------------------------------------------
Observations        30            30      
R2                0.713          0.713    
chi2 (df = 3)   37.238***      37.238***  
==========================================
Note:          *p<0.1; **p<0.05; ***p<0.01
于 2015-06-30T11:22:56.957 回答