Lower and upper thresholds for predicted values — layer

This postprocessing step is used to set prediction values that are smaller than the lower threshold or higher than the upper threshold equal to the threshold values.

Usage

layer_threshold(
  frosting,
  ...,
  lower = 0,
  upper = Inf,
  id = rand_id("threshold")
)

Arguments

frosting: a frosting postprocessor
...: <tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables. Typical usage is .pred to threshold predictions to a range (say, nonnegative).
lower: Lower threshold for the prediction values. That is, any predictions that are less than this lower bound are set to it. Default value is 0.
upper: Upper threshold for the prediction values. That is, any predictions that are greater than this upper bound are set to it. Default value is Inf.
id: a random id string

Value

an updated frosting postprocessor

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  filter(time_value < "2021-03-08", geo_value %in% c("ak", "ca", "ar"))
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_threshold(.pred, lower = 0.180, upper = 0.310)
wf <- wf %>% add_frosting(f)
p <- forecast(wf)
p
#> An `epi_df` object, 3 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 3
#>   geo_value time_value .pred
#> * <chr>     <date>     <dbl>
#> 1 ak        2021-03-07  0.18
#> 2 ar        2021-03-07  0.18
#> 3 ca        2021-03-07  0.31