R预测包VS斯塔塔边距 [英] R prediction package VS Stata margins

问题描述

我从Stata切换到R，当我使用预测来计算边距pred和Stata命令 margins 的结果时发现不一致的结果，将变量的值固定为 x .这是示例:

I'm switching from Stata to R, and I find inconsistent results when I use prediction to compute marginal pred and the results from the Stata command margins fixing the values of a variable to x. Here is the example:

library(dplyr)
library(prediction)

d <- data.frame(x1 = factor(c(1,1,1,2,2,2), levels = c(1, 2)),
            x2 = factor(c(1,2,3,1,2,3), levels = c(1, 2, 3)),
            x3 = factor(c(1,2,1,2,1,2), levels = c(1, 2)),
            y = c(3.1, 2.8, 2.5, 4.3, 4.0, 3.5))

m2 <- lm(y ~ x1 + x2 + x3, d)
summary(m2)

marg2a <- prediction(m2, at = list(x2 = "1"))
marg2b <- prediction(m2, at = list(x1 = "1"))

marg2a %>%
  select(x1, fitted) %>%
  group_by(x1) %>%
  summarise(error = mean(fitted))

marg2b %>%
  select(x2, fitted) %>%
  group_by(x2) %>%
  summarise(error = mean(fitted))

这是结果:

# A tibble: 2 x 2
      x1    error
   <fctr>    <dbl>
1      1 3.133333
2      2 4.266667


# A tibble: 3 x 2
      x2 error
  <fctr> <dbl>
1      1 3.125
2      2 2.825
3      3 2.425

如果我尝试使用Stata的边距复制它，则结果是:

while if I try to replicate this using Stata's margins, this is the result:

regress y i.x1 i.x2 i.x3
margins i.x1, at(x2 == 1)
margins i.x2, at(x1 == 1)


------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x1 |
          1  |      3.125   .0829157    37.69   0.017     2.071456    4.178544
          2  |      4.275   .0829157    51.56   0.012     3.221456    5.328544
------------------------------------------------------------------------------

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x2 |
          1  |      3.125   .0829157    37.69   0.017     2.071456    4.178544
          2  |      2.825   .0829157    34.07   0.019     1.771456    3.878544
          3  |      2.425   .0829157    29.25   0.022     1.371456    3.478544
------------------------------------------------------------------------------

R和Stata中x2的边距相同，但是当涉及x1时，存在差异，我不知道为什么.非常感谢您的帮助.谢谢，

The margins for x2 are the same in R and Stata, but when it comes to x1 there are differences and I don't know why. Really appreciate any help. Thanks,

P

推荐答案

您的Stata和R代码不相同.要复制该Stata代码，您需要:

Your Stata and R code are not equivalent. To replicate that Stata code you would need:

> prediction(m2, at = list(x1 = c("1", "2"), x2 = "1"))
Average predictions for 6 observations:
 at(x1) at(x2) value
      1      1 3.125
      2      1 4.275
> prediction(m2, at = list(x2 = c("1", "2", "3"), x1 = "1"))
Average predictions for 6 observations:
 at(x2) at(x1) value
      1      1 3.125
      2      1 2.825
      3      1 2.425

这是因为当您说margins i.x1时，您要求对数据集的反事实版本进行预测，其中x1被替换为1，然后被替换为2，并且附加的约束条件是两个反事实x2都保持为1在您的第二个Stata示例中也发生了同样的事情.

That is because when you say margins i.x1 you are asking for predictions for counterfactual versions of the dataset where x1 is replaced with 1 and then replaced with 2, with the additional constraint that in both counterfactual x2 is held at 1. The same thing is occurring in your second Stata example.

这是由于以下事实:Stata的margins命令具有歧义，或者两个语法表达式获得相同的输出.一个是您的代码:

This is due to the fact that Stata's margins command has an ambiguity or rather two syntactic expressions that obtain the same output. One is your code:

. margins i.x1, at(x2 == 1)

Predictive margins                              Number of obs     =          6
Model VCE    : OLS

Expression   : Linear prediction, predict()
at           : x2              =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          x1 |
          1  |      3.125   .0829156    37.69   0.017     2.071457    4.178543
          2  |      4.275   .0829156    51.56   0.012     3.221457    5.328543
------------------------------------------------------------------------------

另一个是关于上面实际发生的事情的更明确的说明:

The other is more explicit about what is actually happening in the above:

. margins, at(x1 = (1 2) x2 == 1)

Predictive margins                              Number of obs     =          6
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : x1              =           1
               x2              =           1

2._at        : x1              =           2
               x2              =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |      3.125   .0829156    37.69   0.017     2.071457    4.178543
          2  |      4.275   .0829156    51.56   0.012     3.221457    5.328543
------------------------------------------------------------------------------

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