r - tidy() 工作但 glance() 和 augment() 在回归模型中不起作用

Question

我应用下面的代码，

代码:

rollingLinearModels <- rollify(
  .f = function(price, date){
    lm(price ~ date)
  },
  window = 30,
  unlist = FALSE
)
out <- d %>%
  mutate(
    date = as.Date(date),
    logPrice = log(Adjusted)
  ) %>% 
  group_by(.id) %>% 
  mutate(
    models = rollingLinearModels(logPrice, date)
  ) %>% 
  mutate(tidymodels = map(models, ~tidy(.x)),
         #glancemodels = map(models, ~glance(.x))
         #augmentmodels = map(models, ~augment(.x))
  )

效果很好，但是当我取消注释代码的 glance 和 augment 部分时，我遇到了错误。

Error: mutate() argument glancemodels errored. ℹ glancemodels is map(models, ~glance(.x)). ℹ The error occured in group 1: .id = "BLK". x No glance method for objects of class logical

为什么我可以应用 tidy() 而不能应用其他两种方法？我想使用模型中的 r.squared。

数据:

d <- structure(list(.id = c("BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", 
"BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "BLK", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
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"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", 
"MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT"), date = c("2019-01-02", 
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16477200, 14209100, 19745100, 15792600, 14071800, 19300900, 27991000, 
15648700, 24025500, 31257000, 38033900, 23654900, 16324200, 24166500, 
26821700, 27350200, 24911100, 24239800, 36017700, 28419000, 27235800, 
30915100, 33944900, 25266300, 24722700, 30112200, 25770500, 23706900, 
15293300, 15396500, 23603800, 14123400, 23128400, 22763100, 16829600, 
26646800), Adjusted = c(377.922577, 366.820343, 380.25174, 381.309509, 
386.161896, 388.374573, 387.704956, 386.161896, 384.715912, 388.879242, 
400.845215, 400.340515, 407.065948, 401.388672, 396.41983, 394.556519, 
397.846466, 393.391907, 395.585205, 396.274231, 402.824951, 404.503876, 
405.639313, 405.23175, 404.096313, 402.75705, 399.602966, 400.515259, 
412.947021, 414.829742, 410.879883, 419.196869, 418.342896, 424.311279, 
422.176208, 423.82608, 425.970764, 427.52356, 428.9599, 430.134155, 
430.667877, 424.41803, 422.941467, 420.340393, 413.818085, 411.979736, 
418.619354, 418.365112, 422.27655, 424.026886, 423.948669, 430.72522, 
424.946075, 420.399078, 420.829285, 408.117188, 406.210388, 413.309601, 
410.34671, 415.558655, 417.905518, 428.681488, 426.784454, 429.346436, 
432.055054, 435.242859, 436.259857, 430.070007, 432.954712, 436.230499, 
444.288055, 441.853119, 456.208069, 457.137024, 455.376892, 453.743835, 
463.532196, 469.536224, 463.942902, 468.372589, 469.213531, 474.493958, 
468.519257, 465.859497, 471.814606, 465.820374, 453.49939, 455.161743, 
453.939453, 452.746429, 432.974243, 435.986053, 434.519257, 437.33548, 
431.78125, 428.388092, 431.879059, 429.414886, 424.281158, 426.109711, 
419.802582, 418.453094, 417.142792, 406.357056, 99.375191, 95.719376, 
100.171211, 100.298965, 101.026199, 102.470825, 101.812401, 101.026199, 
100.289146, 103.198067, 103.561684, 104.288918, 105.851479, 103.856506, 
104.868729, 104.367531, 105.320793, 103.266861, 101.163788, 104.544426, 
102.628075, 101.006546, 103.915466, 105.369934, 104.20047, 103.453575, 
103.84668, 103.433922, 105.045631, 104.967003, 105.05545, 106.352676, 
106.303543, 105.750854, 107.981354, 109.520973, 110.132874, 110.892822, 
110.705299, 110.567131, 111.060608, 110.794128, 110.241432, 110.290787, 
108.94854, 109.066978, 111.356689, 112.136368, 113.004875, 113.093697, 
114.396469, 116.034782, 116.113747, 115.985443, 118.650192, 115.521576, 
116.123627, 116.370346, 115.245224, 115.403145, 116.399963, 117.465858, 
117.633636, 118.403458, 117.801422, 118.324493, 118.363968, 117.72245, 
118.620583, 118.758751, 119.370651, 119.469353, 119.193001, 120.179947, 
121.759056, 122.143959, 123.802032, 123.377647, 127.463577, 128.193909, 
128.075485, 128.894653, 126.210159, 124.561966, 127.216843, 126.476624, 
123.880966, 123.871117, 123.861244, 125.469955, 121.739311, 123.101303, 
124.834839, 127.717468, 126.86557, 125.032967, 125.706573, 126.469322, 
124.99334, 125.052765, 124.973534, 123.764999, 124.547562, 122.516846
)), class = "data.frame", row.names = c(NA, -208L))

Answer 1

这个问题是基于一些list元素中的NA_logical_，我们可以使用if/else条件

library(dplyr) #devel version
library(broom)
library(tibbletime)
library(purrr)
d %>%
   mutate(date = as.Date(date),  logPrice = log(Adjusted)) %>% 
   group_by(.id) %>% 
   condense(models = rollingLinearModels(logPrice, date), 
            tidymodels = map(models, tidy), 
            glancemodels = map(models, ~ if(!is.na(.x)) glance(.x) else NA),
            augmentmodels =  map(models, ~ if(!is.na(.x)) augment(.x) else NA))
# A tibble: 2 x 5
# Rowwise:  .id
#  .id   models       tidymodels   glancemodels augmentmodels
#  <chr> <list>       <list>       <list>       <list>       
#1 BLK   <list [104]> <list [104]> <list [104]> <list [104]> 
#2 MSFT  <list [104]> <list [104]> <list [104]> <list [104]> 

---

如果我们对单个元素执行此操作，它将能够复制

 d %>% 
   mutate(date = as.Date(date),  logPrice = log(Adjusted)) %>% 
   group_by(.id) %>% 
   condense(models = rollingLinearModels(logPrice, date)) %>% 
   .$models %>% 
   magrittr::extract2(1) %>% 
   magrittr::extract2(104) %>% 
   glance
# A tibble: 1 x 11
#  r.squared adj.r.squared  sigma statistic  p.value    df logLik   AIC   BIC deviance df.residual
#      <dbl>         <dbl>  <dbl>     <dbl>    <dbl> <int>  <dbl> <dbl> <dbl>    <dbl>       <int>
#1     0.820         0.813 0.0195      127. 6.31e-12     2   76.6 -147. -143.   0.0106          28

现在，在 NA

的元素上尝试

d %>% 
   mutate(date = as.Date(date),  logPrice = log(Adjusted)) %>% 
   group_by(.id) %>% 
   condense(models = rollingLinearModels(logPrice, date)) %>%
   .$models %>%
   magrittr::extract2(1) %>% 
   magrittr::extract2(1) 
#[1] NA
d %>%
    mutate(date = as.Date(date),  logPrice = log(Adjusted)) %>% 
    group_by(.id) %>% 
    condense(models = rollingLinearModels(logPrice, date)) %>%
    .$models %>% 
    magrittr::extract2(1) %>% 
    magrittr::extract2(1) %>% 
    glance

Error: No glance method for objects of class logical

tidy 之所以有效，是因为它创建了一个 data.frame，其中包含具有“NA”值的单个列“x”

d %>% 
   mutate(date = as.Date(date),  logPrice = log(Adjusted)) %>%
   group_by(.id) %>%
   condense(models = rollingLinearModels(logPrice, date)) %>% 
   .$models %>% 
   magrittr::extract2(1) %>%
   magrittr::extract2(1) %>% 
   tidy
# A tibble: 1 x 1
#  x    
#  <lgl>
#1 NA   

---

此外，通过检查每个函数的方法的数量

length(methods("tidy"))
#[1] 115
length(methods("glance"))
#[1] 64
length(methods('augment'))
#[1] 36

所以，'tidy' 可能会处理很多情况，尤其是有一种方法 tidy.NULL*