Creates predictions based on residual quantiles

Usage

layer_residual_quantiles(
  frosting,
  ...,
  quantile_levels = c(0.05, 0.95),
  symmetrize = TRUE,
  by_key = character(0L),
  name = ".pred_distn",
  id = rand_id("residual_quantiles")
)

Arguments

frosting: a frosting postprocessor
...: Unused, include for consistency with other layers.
quantile_levels: numeric vector of probabilities with values in (0,1) referring to the desired quantile.
symmetrize: logical. If TRUE then interval will be symmetric.
by_key: A character vector of keys to group the residuals by before calculating quantiles. The default, c() performs no grouping.
name: character. The name for the output column.
id: a random id string

Value

an updated frosting postprocessor with additional columns of the residual quantiles added to the prediction

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  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, linear_reg()) %>% fit(jhu)

f <- frosting() %>%
  layer_predict() %>%
  layer_residual_quantiles(
    quantile_levels = c(0.0275, 0.975),
    symmetrize = FALSE
  ) %>%
  layer_naomit(.pred)
wf1 <- wf %>% add_frosting(f)

p <- forecast(wf1)

f2 <- frosting() %>%
  layer_predict() %>%
  layer_residual_quantiles(
    quantile_levels = c(0.3, 0.7),
    by_key = "geo_value"
  ) %>%
  layer_naomit(.pred)
wf2 <- wf %>% add_frosting(f2)

p2 <- forecast(wf2)
#> Warning: Some grouping keys are not in data.frame returned by the
#> `residuals()` method. Groupings may not be correct.