线性混合加权回归中 R^2 的 95% CI

Question

I am trying to calculate the R^2 and its 95%CI in linear mixed weighted regression. Since the summary of lme() doesn't provide R^2, I am using the r.squaredGLMM() from MuMIn package, and boot() from boot package. (If you have better way to do this, please let me know!!!) However, I found the upper and lower bound to be the same number. Why? I am using the mtcars data as an example here:

library(lme4)
library(boot)
library(MuMIn)

foo <- boot(mtcars, function(data, indices) 
  r.squaredGLMM(lme(mpg ~ wt, data=mtcars, 
                    random= ~1|gear, weights= ~carb))[1], R=1000)
foo$t0
quantile(foo$t,  c(0.025, 0.975))

Answer 1

原来我忘了添加data=mtcars[indices,] ... 我想删除这个问题，但我想也许它可以帮助 R 的新人有类似的问题。