6

我一直在使用整洁的预测包寓言（非常有用）。

我想知道是否有一种简单的方法可以从 mable 中提取 p、d、q 值。

以本指南中的数据为例https://www.mitchelloharawild.com/blog/fable/

library(tidyverse)
library(tsibble)
library(fable)

tourism_state <- tourism %>% 
  group_by(State) %>% 
  summarise(Trips = sum(Trips))

fit <- tourism_state %>% 
  model(arima = ARIMA(Trips))

> fit
# A mable: 8 x 2
# Key:     State [8]
  State                                 arima
  <chr>                               <model>
1 ACT                          <ARIMA(0,1,1)>
2 New South Wales    <ARIMA(0,1,1)(0,1,1)[4]>
3 Northern Territory <ARIMA(1,0,1)(0,1,1)[4]>
4 Queensland                   <ARIMA(2,1,2)>
5 South Australia    <ARIMA(1,0,1)(0,1,1)[4]>
6 Tasmania           <ARIMA(0,0,3)(2,1,0)[4]>
7 Victoria           <ARIMA(0,1,1)(0,1,1)[4]>
8 Western Australia            <ARIMA(0,1,3)>

我知道规格存储在 model[[1]]$fit$spec 下，但如果我有大量模型，我无法找到提取它们的方法

理想情况下我想

  State                                 arima       p     d       q
  <chr>                               <model>
1 ACT                          <ARIMA(0,1,1)>       0     1       1
2 New South Wales    <ARIMA(0,1,1)(0,1,1)[4]>       0     1       1
3 Northern Territory <ARIMA(1,0,1)(0,1,1)[4]>       1     0       1
4 Queensland                   <ARIMA(2,1,2)>       
5 South Australia    <ARIMA(1,0,1)(0,1,1)[4]>       and so on....
6 Tasmania           <ARIMA(0,0,3)(2,1,0)[4]>
7 Victoria           <ARIMA(0,1,1)(0,1,1)[4]>
8 Western Australia            <ARIMA(0,1,3)>

谢谢！

4

1 回答 1

6

那这个呢？

# specificly needed libraries from tidyverse
library(dplyr)
library(purrr)

fit %>%
  mutate(map_dfr(arima, c("fit", "spec")))

#> # A mable: 8 x 10
#> # Key:     State [8]
#>   State                                 arima     p     d     q     P     D     Q constant period
#>   <chr>                               <model> <int> <int> <int> <int> <int> <int> <lgl>     <dbl>
#> 1 ACT                          <ARIMA(0,1,1)>     0     1     1     0     0     0 FALSE         4
#> 2 New South Wales    <ARIMA(0,1,1)(0,1,1)[4]>     0     1     1     0     1     1 FALSE         4
#> 3 Northern Territory <ARIMA(1,0,1)(0,1,1)[4]>     1     0     1     0     1     1 FALSE         4
#> 4 Queensland                   <ARIMA(2,1,2)>     2     1     2     0     0     0 FALSE         4
#> 5 South Australia    <ARIMA(1,0,1)(0,1,1)[4]>     1     0     1     0     1     1 FALSE         4
#> 6 Tasmania           <ARIMA(0,0,3)(2,1,0)[4]>     0     0     3     2     1     0 FALSE         4
#> 7 Victoria           <ARIMA(0,1,1)(0,1,1)[4]>     0     1     1     0     1     1 FALSE         4
#> 8 Western Australia            <ARIMA(0,1,3)>     0     1     3     0     0     0 FALSE         4

它适用于R >= 4.0和dplyr >= 1.0。

该arima列是一个列表。我们可以用来map从列表中提取数据。

map将返回一个列表本身，但map_dfr您可以返回一个数据框，该数据框mutate将解释为一组新列以添加到原始数据框。

请注意，使用此代码，输出和输入保持相同的类 ( mable)。

于 2020-08-13T10:08:55.887 回答