数据集上的R循环和存储模型系数

你的设置有一些奇怪的地方--你没有学校数据集的列表，你有学校名称的列表？说到"系数"，你的意思是你只对斜率感兴趣(抛开截距？)为什么你的模型中有一个预测变量x1，而它不在你的数据中……

library(broom)
library(tidyverse)
schoolnames <- c('school1', 'school2', 'school3')
schools <- mget(schoolnames)
res <- vector(length = 3, mode = "list")
names(res) <- schoolnames
for(i in seq_along(schools)){
      tmp <- lm(score ~ student, data = schools[[i]])
      res[[i]] <- (tidy(tmp, conf.int = TRUE)
           |> filter(term == "student")
           |> select(estimate, conf.low, conf.high)
      )
    }
WANT <- bind_rows(res, .id = "school")

你也可以用purrr::map()来做这个...

如果出于某种原因，你想以一种低技术含量的方式做到这一点，你可以:

res <- data.frame(schools = schoolnames, est = rep(NA,3),
                  lwr = rep(NA,3), upr = rep(NA,3))
for(i in seq_along(schools)){
      tmp <- lm(score ~ student, data = schools[[i]])
      ## use element 2/row 2 to pick out the slope coefficient/CIs
      res[i,1] <- coef(tmp)[2]
      res[i,2] <- confint(tmp)[2,1]  ## lower CI in column 1
      res[i,3] <- confint(tmp)[2,2]
}