Returns predictive quantiles — layer_quantile

This function calculates quantiles when the prediction was distributional.

Usage

layer_quantile_distn(
  frosting,
  ...,
  quantile_levels = c(0.25, 0.75),
  truncate = c(-Inf, Inf),
  name = ".pred_distn",
  id = rand_id("quantile_distn")
)

Arguments

frosting: a frosting postprocessor
...: Unused, include for consistency with other layers.
quantile_levels: a vector of probabilities to extract
truncate: Do we truncate the distribution to an interval
name: character. The name for the output column.
id: a random id string

Value

an updated frosting postprocessor. An additional column of predictive quantiles will be added to the predictions.

Details

Currently, the only distributional modes/engines are

quantile_reg()
smooth_quantile_reg()
rand_forest(mode = "regression") %>% set_engine("grf_quantiles")

If these engines were used, then this layer will grab out estimated (or extrapolated) quantiles at the requested quantile values.

Examples

library(dplyr)
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, quantile_reg(quantile_levels = c(.25, .5, .75))) %>%
  fit(jhu)

f <- frosting() %>%
  layer_predict() %>%
  layer_quantile_distn() %>%
  layer_naomit(.pred)
wf1 <- wf %>% add_frosting(f)

p <- forecast(wf1)
p
#> An `epi_df` object, 3 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * other_keys = geo_value, time_value
#> * as_of     = 2022-05-31
#> 
#> # A tibble: 3 × 4
#>   geo_value time_value              .pred        .pred_distn
#> * <chr>     <date>                 <dist>             <dist>
#> 1 ak        2021-12-31 quantiles(0.17)[3] quantiles(0.12)[2]
#> 2 ca        2021-12-31 quantiles(0.18)[3] quantiles(0.21)[2]
#> 3 ny        2021-12-31 quantiles(0.27)[3] quantiles(0.25)[2]