impute_linear.md
In 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.

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.

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

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