Adjust a step in an epi_workflow or epi_recipe

Make a parameter adjustment to a step in either an epi_workflow or epi_recipe object.

Usage

adjust_epi_recipe(
  x,
  which_step,
  ...,
  blueprint = default_epi_recipe_blueprint()
)

# S3 method for class 'epi_workflow'
adjust_epi_recipe(
  x,
  which_step,
  ...,
  blueprint = default_epi_recipe_blueprint()
)

# S3 method for class 'epi_recipe'
adjust_epi_recipe(
  x,
  which_step,
  ...,
  blueprint = default_epi_recipe_blueprint()
)

Arguments

x

A epi_workflow or epi_recipe object

which_step

the number or name of the step to adjust

...

Used to input a parameter adjustment

blueprint

A hardhat blueprint used for fine tuning the preprocessing.

Value

x, updated with the adjustment to the specified epi_recipe step.

Details

This function can either adjust a step in a epi_recipe object or a step from a epi_recipe object in an epi_workflow. The step to be adjusted is indicated by either the step number or name (if a name is used, it must be unique). In either case, the argument name and update value must be inputted as .... See the examples below for brief illustrations of the different types of updates.

Examples

library(dplyr)
library(workflows)
#> 
#> Attaching package: ‘workflows’
#> The following objects are masked from ‘package:epipredict’:
#> 
#>     add_model, remove_model, update_model

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, parsnip::linear_reg()) %>% fit(jhu)
latest <- jhu %>%
  filter(time_value >= max(time_value) - 14)

# Adjust `step_epi_ahead` to have an ahead value of 14
# in the `epi_workflow`
# Option 1. Using the step number:
wf2 <- wf %>% adjust_epi_recipe(which_step = 2, ahead = 14)
extract_preprocessor(wf2)
#> 
#> ── Epi Recipe ──────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> raw:        2
#> geo_value:  1
#> time_value: 1
#> 
#> ── Operations 
#> 1. Lagging: death_rate by 0, 7, 14
#> 2. Leading: death_rate by 14
#> 3. • Removing rows with NA values in: all_predictors()
#> 4. • Removing rows with NA values in: all_outcomes()
# Option 2. Using the step name:
wf3 <- wf %>% adjust_epi_recipe(which_step = "step_epi_ahead", ahead = 14)
extract_preprocessor(wf3)
#> 
#> ── Epi Recipe ──────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> raw:        2
#> geo_value:  1
#> time_value: 1
#> 
#> ── Operations 
#> 1. Lagging: death_rate by 0, 7, 14
#> 2. Leading: death_rate by 14
#> 3. • Removing rows with NA values in: all_predictors()
#> 4. • Removing rows with NA values in: all_outcomes()

# Adjust `step_epi_ahead` to have an ahead value of 14
# in the `epi_recipe`
# Option 1. Using the step number
r2 <- r %>% adjust_epi_recipe(which_step = 2, ahead = 14)
r2
#> 
#> ── Epi Recipe ──────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> raw:        2
#> geo_value:  1
#> time_value: 1
#> 
#> ── Operations 
#> 1. Lagging: death_rate by 0, 7, 14
#> 2. Leading: death_rate by 14
#> 3. • Removing rows with NA values in: all_predictors()
#> 4. • Removing rows with NA values in: all_outcomes()
# Option 2. Using the step name
r3 <- r %>% adjust_epi_recipe(which_step = "step_epi_ahead", ahead = 14)
r3
#> 
#> ── Epi Recipe ──────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> raw:        2
#> geo_value:  1
#> time_value: 1
#> 
#> ── Operations 
#> 1. Lagging: death_rate by 0, 7, 14
#> 2. Leading: death_rate by 14
#> 3. • Removing rows with NA values in: all_predictors()
#> 4. • Removing rows with NA values in: all_outcomes()