I do not have a workaround for this at the moment, so desperately looking to solve this issue, no matter how cumbersome as long as my code is working again...
I want to coerce a tsibble to a fable object with:
as_fable
Documentation says that this is possible:
## S3 method for class 'tbl_ts'
as_fable(x, response, distribution, ...)
But when I specify the input parameter of this function I always get an error.
Example:
library(tsibbledata)
library(tsibble)
library(fable)
library(fabletools)
aus <- tsibbledata::hh_budget
fit <- fabletools::model(aus, ARIMA = ARIMA(Debt))
fc_tsibble <- fit %>%
fabletools::forecast(., h = 2) %>%
as_tibble(.) %>%
tsibble::as_tsibble(., key = c(Country, .model), index = Year)
fc_tsibble
# A tsibble: 8 x 5 [1Y]
# Key: Country, .model [4]
Country .model Year Debt .mean
<chr> <chr> <dbl> <dist> <dbl>
1 Australia ARIMA 2017 N(215, 21) 215.
2 Australia ARIMA 2018 N(221, 63) 221.
3 Canada ARIMA 2017 N(188, 7) 188.
4 Canada ARIMA 2018 N(192, 21) 192.
5 Japan ARIMA 2017 N(106, 3.8) 106.
6 Japan ARIMA 2018 N(106, 7.6) 106.
7 USA ARIMA 2017 N(109, 11) 109.
8 USA ARIMA 2018 N(110, 29) 110.
class(fc_tsibble)
[1] "tbl_ts" "tbl_df" "tbl" "data.frame"
Coercing to a fable leads to an error:
as_fable(fc_tsibble, response = .mean, distribution = Debt)
Error in eval_tidy(enquo(response)) : object '.mean' not found
Would be extremely grateful for any help!
It's not the most intuitive error message, but I have experienced this before with this function. You actually have to pass Debt to both arguments. I believe the error message references .mean because of an error thrown by an internal function.
library(tsibbledata)
library(tsibble)
library(fable)
library(fabletools)
aus <- tsibbledata::hh_budget
fit <- fabletools::model(aus, ARIMA = ARIMA(Debt))
fc_tsibble <- fit %>%
fabletools::forecast(., h = 2) %>%
as_tibble(.) %>%
tsibble::as_tsibble(., key = c(Country, .model), index = Year)
fc_tsibble
#> # A tsibble: 8 x 5 [1Y]
#> # Key: Country, .model [4]
#> Country .model Year Debt .mean
#> <chr> <chr> <dbl> <dist> <dbl>
#> 1 Australia ARIMA 2017 N(215, 21) 215.
#> 2 Australia ARIMA 2018 N(221, 63) 221.
#> 3 Canada ARIMA 2017 N(188, 7) 188.
#> 4 Canada ARIMA 2018 N(192, 21) 192.
#> 5 Japan ARIMA 2017 N(106, 3.8) 106.
#> 6 Japan ARIMA 2018 N(106, 7.6) 106.
#> 7 USA ARIMA 2017 N(109, 11) 109.
#> 8 USA ARIMA 2018 N(110, 29) 110.
fbl <- as_fable(fc_tsibble, response = "Debt", distribution = "Debt")
fbl
#> # A fable: 8 x 5 [1Y]
#> # Key: Country, .model [4]
#> Country .model Year Debt .mean
#> <chr> <chr> <dbl> <dist> <dbl>
#> 1 Australia ARIMA 2017 N(215, 21) 215.
#> 2 Australia ARIMA 2018 N(221, 63) 221.
#> 3 Canada ARIMA 2017 N(188, 7) 188.
#> 4 Canada ARIMA 2018 N(192, 21) 192.
#> 5 Japan ARIMA 2017 N(106, 3.8) 106.
#> 6 Japan ARIMA 2018 N(106, 7.6) 106.
#> 7 USA ARIMA 2017 N(109, 11) 109.
#> 8 USA ARIMA 2018 N(110, 29) 110.
Created on 2020-09-28 by the reprex package (v0.3.0)
It also works if you don't quote the distribution variable.
as_fable(fc_tsibble, response = "Debt", distribution = Debt)
#> # A fable: 8 x 5 [1Y]
#> # Key: Country, .model [4]
#> Country .model Year Debt .mean
#> <chr> <chr> <dbl> <dist> <dbl>
#> 1 Australia ARIMA 2017 N(215, 21) 215.
#> 2 Australia ARIMA 2018 N(221, 63) 221.
#> 3 Canada ARIMA 2017 N(188, 7) 188.
#> 4 Canada ARIMA 2018 N(192, 21) 192.
#> 5 Japan ARIMA 2017 N(106, 3.8) 106.
#> 6 Japan ARIMA 2018 N(106, 7.6) 106.
#> 7 USA ARIMA 2017 N(109, 11) 109.
#> 8 USA ARIMA 2018 N(110, 29) 110.
Note that in the documentation it specifies that the response argument should be a character vector:
response
The character vector of response variable(s).
However, if you do this you still get an error:
as_fable(fc_tsibble, response = ".mean", distribution = Debt)
#> Error: `fbl[[chr_dist]]` must be a vector with type <distribution>.
#> Instead, it has type <distribution>.
That error message is also unintuitive and somewhat conflicting. This is where I learned that you actually want to pass the distribution column to both arguments:
as_fable(fc_tsibble, response = "Debt", distribution = Debt)
#> # A fable: 8 x 5 [1Y]
#> # Key: Country, .model [4]
#> Country .model Year Debt .mean
#> <chr> <chr> <dbl> <dist> <dbl>
#> 1 Australia ARIMA 2017 N(215, 21) 215.
#> 2 Australia ARIMA 2018 N(221, 63) 221.
#> 3 Canada ARIMA 2017 N(188, 7) 188.
#> 4 Canada ARIMA 2018 N(192, 21) 192.
#> 5 Japan ARIMA 2017 N(106, 3.8) 106.
#> 6 Japan ARIMA 2018 N(106, 7.6) 106.
#> 7 USA ARIMA 2017 N(109, 11) 109.
#> 8 USA ARIMA 2018 N(110, 29) 110.
as_fable(fc_tsibble, response = "Debt", distribution = "Debt")
#> # A fable: 8 x 5 [1Y]
#> # Key: Country, .model [4]
#> Country .model Year Debt .mean
#> <chr> <chr> <dbl> <dist> <dbl>
#> 1 Australia ARIMA 2017 N(215, 21) 215.
#> 2 Australia ARIMA 2018 N(221, 63) 221.
#> 3 Canada ARIMA 2017 N(188, 7) 188.
#> 4 Canada ARIMA 2018 N(192, 21) 192.
#> 5 Japan ARIMA 2017 N(106, 3.8) 106.
#> 6 Japan ARIMA 2018 N(106, 7.6) 106.
#> 7 USA ARIMA 2017 N(109, 11) 109.
#> 8 USA ARIMA 2018 N(110, 29) 110.