This generates a postprocessing container (much like recipes::recipe())
to hold steps for postprocessing predictions.
Examples
library(dplyr)
# Toy example to show that frosting can be created and added for postprocessing
f <- frosting()
wf <- epi_workflow() %>% add_frosting(f)
# A more realistic example
jhu <- covid_case_death_rates %>%
filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
r <- epi_recipe(jhu) %>%
step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
step_epi_ahead(death_rate, ahead = 7) %>%
step_epi_naomit()
wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
f <- frosting() %>%
layer_predict() %>%
layer_naomit(.pred)
wf1 <- wf %>% add_frosting(f)
p <- forecast(wf1)
p
#> An `epi_df` object, 3 x 3 with metadata:
#> * geo_type = state
#> * time_type = day
#> * other_keys = geo_value, time_value
#> * as_of = 2022-05-31
#>
#> # A tibble: 3 × 3
#> geo_value time_value .pred
#> * <chr> <date> <dbl>
#> 1 ak 2021-12-31 0.245
#> 2 ca 2021-12-31 0.313
#> 3 ny 2021-12-31 0.295