R：重新排列列表，purrr

这是关于重新排列列表的：我正在做一个类似于以下代码的交叉验证，其中

cv_chunk

的数量是任意的：

library(purrr)
# randomly assign to a cross validation chunk
set.seed(11)
mtcars$cv_chunk <- sample(rep(1:3), nrow(mtcars), 1)

model_confint <- mtcars %>% 
  split(.$cv_chunk) %>% 
  map(~lm(mpg ~ cyl*qsec + gear - cv_chunk, data = .)) %>% 
  map(confint, levels = 0.95) %>%
  map(t)

  names(model_confint) <- paste0("CV_", names(model_confint))

# first element of the list
$CV_1
       (Intercept)        cyl      qsec      gear   cyl:qsec
2.5 %     -54.8983 -25.691233 -8.958490 -5.175215 -0.7161008
97.5 %    215.2629   9.901694  4.322784  5.185608  1.2804372

我确信使用

apply

函数或

purr

包是一种很好的方法。但是我被卡住了

---

谢谢你的帮助。

谢谢你的好例子！我的两个直觉让这个问题变得更容易：可以使用

purrr:：set_names

在循环中更早地命名列表，也可以通过更早地映射到数据帧来避免转置

库（purrr）
#随机分配给交叉验证块
种子（11）
mtcars$cv_%
分割（.$cv_块）%>%
#在此处命名列表，以便稍后将其用作参数
设置名称（粘贴0（“CV”），名称（.））%>%
#映射实际列表和列表名称以创建一个数据帧
地图（~lm（mpg~cyl*qsec+gear-cv_区块，数据=））%>%
map2_dfr（.x=，.y=名称（.），函数（x，y）{
df$`（截获）`
#>2.5%97.5%cv\U块
#>1-54.8983215.26286 CV_1
#>6-193.2069 84.48072 CV_2
#>11-361.1489 545.04010 CV_3
#> 
#>$cyl
#>2.5%97.5%cv\U块
#>2-25.69123 9.901694 CV_1
#>7-11.44012 24.073391 CV_2
#>12-26.36288 16.589356 CV_3
#> 
#>$`cyl:qsec`
#>2.5%97.5%cv\U块
#>5-0.7161008 1.2804372 CV_1
#>10-1.4870954 0.5295074 CV_2
#>15纳纳库夫3
#> 
#>$gear
#>2.5%97.5%cv\U块
#>4-5.175215 5.185608 CV_1
#>9-3.975621 8.092960 CV_2
#>14-30.785104 33.533967 CV_3
#> 
#>$qsec
#>2.5%97.5%cv\U块
#>3-8.958490 4.322784 CV_1
#>8-2.257827 11.261528 CV_2
#>13-17.741244 12.929339 CV_3

这可以使用

扫帚来完成：

library(purrr)
library(dplyr)
library(tidyr)
# randomly assign to a cross validation chunk
set.seed(11)
mtcars$cv_chunk <- sample(seq(3), nrow(mtcars), replace = TRUE)

mtcars %>% 
  split(.$cv_chunk) %>% 
  map(~lm(mpg ~ cyl*qsec + gear - cv_chunk, data = .)) %>% 
  # the following will work uder different seed. I will report a bug to `broom``
  #map_dfr(~broom::tidy(.x, conf.int=TRUE), .id="cv_chunk")
  map_dfr(~bind_cols(broom::tidy(.x), drop_na(broom::confint_tidy(.x))), .id="cv_chunk") %>% 
  select(cv_chunk, term, conf.low, conf.high) %>% 
  split(.$term) 

library(purrr)
library(dplyr)
library(tidyr)
# randomly assign to a cross validation chunk
set.seed(11)
mtcars$cv_chunk <- sample(seq(3), nrow(mtcars), replace = TRUE)

mtcars %>% 
  split(.$cv_chunk) %>% 
  map(~lm(mpg ~ cyl*qsec + gear - cv_chunk, data = .)) %>% 
  # the following will work uder different seed. I will report a bug to `broom``
  #map_dfr(~broom::tidy(.x, conf.int=TRUE), .id="cv_chunk")
  map_dfr(~bind_cols(broom::tidy(.x), drop_na(broom::confint_tidy(.x))), .id="cv_chunk") %>% 
  select(cv_chunk, term, conf.low, conf.high) %>% 
  split(.$term) 

$`(Intercept)`
   cv_chunk        term  conf.low conf.high
1         1 (Intercept)  -54.8983 215.26286
6         2 (Intercept) -193.2069  84.48072
11        3 (Intercept) -361.1489 545.04010

$cyl
   cv_chunk term  conf.low conf.high
2         1  cyl -25.69123  9.901694
7         2  cyl -11.44012 24.073391
12        3  cyl -26.36288 16.589356

$`cyl:qsec`
   cv_chunk     term   conf.low conf.high
5         1 cyl:qsec -0.7161008 1.2804372
10        2 cyl:qsec -1.4870954 0.5295074

$gear
   cv_chunk term   conf.low conf.high
4         1 gear  -5.175215  5.185608
9         2 gear  -3.975621  8.092960
14        3 gear -30.785104 33.533967

$qsec
   cv_chunk term   conf.low conf.high
3         1 qsec  -8.958490  4.322784
8         2 qsec  -2.257827 11.261528
13        3 qsec -17.741244 12.929339