# 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]
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