如何使用R中的confint計算95％CI？

Question

我用confint幫助頁面

> fit <- lm(100/mpg ~ disp + hp + wt + am, data=mtcars) 
> summary(fit) 

Call: 
lm(formula = 100/mpg ~ disp + hp + wt + am, data = mtcars) 

Residuals: 
    Min  1Q Median  3Q  Max 
-1.6923 -0.3901 0.0579 0.3649 1.2608 

Coefficients: 
      Estimate Std. Error t value Pr(>|t|) 
(Intercept) 0.740648 0.738594 1.003 0.32487 
disp  0.002703 0.002715 0.996 0.32832 
hp   0.005275 0.003253 1.621 0.11657 
wt   1.001303 0.302761 3.307 0.00267 ** 
am   0.155815 0.375515 0.415 0.68147 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.6754 on 27 degrees of freedom 
Multiple R-squared: 0.8527,  Adjusted R-squared: 0.8309 
F-statistic: 39.08 on 4 and 27 DF, p-value: 7.369e-11 

> confint(fit) 
        2.5 %  97.5 % 
(Intercept) -0.774822875 2.256118188 
disp  -0.002867999 0.008273849 
hp   -0.001400580 0.011949674 
wt   0.380088737 1.622517536 
am   -0.614677730 0.926307310 
> confint(fit, "wt") 
     2.5 % 97.5 % 
> wt 0.3800887 1.622518 

>confint.default(fit,"wt") 
     2.5 % 97.5 % 
wt 0.4079023 1.594704 

> 1.001303 + 1.96*0.302761 
[1] 1.594715 
> 1.001303 - 1.96*0.302761 
[1] 0.4078914 

所以從confint.default獲得的95％CI是基於漸近正提供的示例。那麼confint呢？

感謝

Answer 1

您可以檢查出的代碼爲每個方法。

# View code for 'default' 
confint.default 
# View code of lm objects 
getAnywhere(confint.lm) 

區別似乎是默認使用正態分位數，線性模型的方法使用T-quantiles來代替。