tests/testthat/_snaps/impute_linear.md

In tidymodels/recipes: Preprocessing and Feature Engineering Steps for Modeling

Fails when one of the variables to impute is non-numeric.

Code
  recipe(tg_dat) %>% step_impute_linear(supp, impute_with = c("len")) %>% prep(
    tg_dat)
Condition
  Error in `step_impute_linear()`:
  Caused by error in `prep()`:
  ! Variable `supp` chosen for linear regression imputation must be of type numeric. Not a string.

Code
  recipe(tg_dat) %>% step_impute_linear(supp, dose, impute_with = c("len")) %>%
    prep(tg_dat)
Condition
  Error in `step_impute_linear()`:
  Caused by error in `prep()`:
  ! Variable `supp` chosen for linear regression imputation must be of type numeric. Not a string.

case weights

Code
  rec_prepped
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  case_weights:    1
  undeclared role: 2

  -- Training information 
  Training data contained 2930 data points and 556 incomplete rows.

  -- Operations 
  * Linear regression imputation for: Lot_Frontage | Trained, weighted

Code
  rec_prepped
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  case_weights:    1
  undeclared role: 2

  -- Training information 
  Training data contained 2930 data points and 556 incomplete rows.

  -- Operations 
  * Linear regression imputation for: Lot_Frontage | Trained, ignored weights

impute_with errors with nothing selected

Code
  recipe(~., data = mtcars) %>% step_impute_linear(all_predictors(), impute_with = NULL) %>%
    prep()
Condition
  Error in `step_impute_linear()`:
  ! `impute_with` must not be empty.

warns if impute_with columns contains missing values

Code
  tmp <- recipe(~., data = mtcars) %>% step_impute_linear(mpg, impute_with = imp_vars(
    disp)) %>% prep()
Condition
  Warning:
  There were missing values in the predictor(s) used to impute; imputation did not occur.

errors if there are no rows without missing values

Code
  recipe(~., data = mtcars) %>% step_impute_linear(all_predictors()) %>% prep()
Condition
  Error in `step_impute_linear()`:
  Caused by error in `prep()`:
  ! The data did not have any rows where the imputation values were all complete. Is is thus unable to fit the linear regression model.

bake method errors when needed non-standard role columns are missing

Code
  bake(rec, new_data = ames_dat[, 2:3])
Condition
  Error in `step_impute_linear()`:
  ! The following required column is missing from `new_data`: Lot_Frontage.

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * Linear regression imputation for: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
  * Linear regression imputation for: <none> | Trained

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  undeclared role: 3

  -- Operations 
  * Linear regression imputation for: Lot_Frontage

Code
  prep(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  undeclared role: 3

  -- Training information 
  Training data contained 2930 data points and 556 incomplete rows.

  -- Operations 
  * Linear regression imputation for: Lot_Frontage | Trained

tidymodels/recipes documentation built on Jan. 25, 2025, 7:30 a.m.