# 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.

# Printing

    Code
      print(imputed)
    Output
      Recipe
      
      Inputs:
      
        3 variables (no declared roles)
      
      Operations:
      
      Linear regression imputation for Lot_Frontage

---

    Code
      prep(imputed)
    Output
      Recipe
      
      Inputs:
      
        3 variables (no declared roles)
      
      Training data contained 2930 data points and 556 incomplete rows. 
      
      Operations:
      
      Linear regression imputation for Lot_Frontage [trained]

# empty printing

    Code
      rec
    Output
      Recipe
      
      Inputs:
      
            role #variables
         outcome          1
       predictor         10
      
      Operations:
      
      Linear regression 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:
      
      Linear regression imputation for <none> [trained]

# case weights

    Code
      rec_prepped
    Output
      Recipe
      
      Inputs:
      
               role #variables
       case_weights          1
      
        2 variables with undeclared roles
      
      Training data contained 2930 data points and 556 incomplete rows. 
      
      Operations:
      
      Linear regression imputation for Lot_Frontage [weighted, trained]

---

    Code
      rec_prepped
    Output
      Recipe
      
      Inputs:
      
               role #variables
       case_weights          1
      
        2 variables with undeclared roles
      
      Training data contained 2930 data points and 556 incomplete rows. 
      
      Operations:
      
      Linear regression imputation for Lot_Frontage [ignored weights, trained]

