# bad args
Code
recipe(~., data = example_data) %>% step_impute_roll(all_predictors(), window = 3) %>%
prep(training = example_data)
Condition
Error in `step_impute_roll()`:
Caused by error in `prep()`:
! All columns selected for the step should be double.
---
Code
recipe(~., data = example_data) %>% update_role(day, new_role = "time_index") %>%
step_impute_roll(all_predictors(), window = 4) %>% prep(training = example_data)
Condition
Error in `step_impute_roll()`:
! `window` should be an odd integer >= 3
---
Code
recipe(~., data = example_data) %>% update_role(day, new_role = "time_index") %>%
step_impute_roll(all_predictors(), window = 3) %>% prep(training = example_data)
Condition
Error in `step_impute_roll()`:
Caused by error in `prep()`:
! All columns selected for the step should be double.
# Deprecation warning
Code
recipe(~., data = mtcars) %>% step_rollimpute()
Condition
Error:
! `step_rollimpute()` was deprecated in recipes 0.1.16 and is now defunct.
i Please use `step_impute_roll()` instead.
# printing
Code
print(seven_pt)
Output
Recipe
Inputs:
role #variables
predictor 3
time_index 1
Operations:
Rolling imputation for all_predictors()
---
Code
prep(seven_pt)
Output
Recipe
Inputs:
role #variables
predictor 3
time_index 1
Training data contained 12 data points and 7 incomplete rows.
Operations:
Rolling imputation for x1, x2, x3 [trained]
# empty printing
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Operations:
Rolling imputation for <none>
---
Code
rec
Output
Recipe
Inputs:
role #variables
outcome 1
predictor 10
Training data contained 32 data points and no missing data.
Operations:
Rolling imputation for <none> [trained]
Generated by dwww version 1.15 on Fri May 24 06:51:44 CEST 2024.