I've been using the tidy forecasting package fable (which has been so useful).
I was wondering if there was an easy way to extract that the p,d,q values from a mable.
Using the data on this guide as an example 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)>
I know the specifications are stored under model[[1]]$fit$spec but I can't figure out a way to extract them if I have a large list of models
Ideally I'd like
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)>
Thanks!
What about this?
# specifically 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
It works on R >= 4.0 and dplyr >= 1.0.
The arima column is a list. We can use map to extract data from lists.
map will return a list itself, but with map_dfr you can return a dataframe which mutate will interprete as a new set of columns to add to the original dataframe.
Note that with this code the output and the input keep the same class (mable).
Since tidyverse changes every 5 days, if you have troubles with this solution (since map_dfr is currently superseded), try with this:
fit %>%
mutate(map(arima, c("fit", "spec")) |> list_rbind())
It should work the same.