# PLS-DA, dense loadings, multiple outcomes Code prep(rec) Condition Error in `step_pls()`: Caused by error in `prep()`: ! `step_pls()` only supports multivariate models for numeric outcomes. # PLS-DA, sparse loadings, multiple outcomes Code prep(rec) Condition Error in `step_pls()`: Caused by error in `prep()`: ! `step_pls()` only supports multivariate models for numeric outcomes. # Deprecation warning Code recipe(~., data = mtcars) %>% step_pls(outcome = "mpg", preserve = TRUE) Condition Error: ! The `preserve` argument of `step_pls()` was deprecated in recipes 0.1.16 and is now defunct. i Please use the `keep_original_cols` argument instead. # print method Code print(rec) Output Recipe Inputs: role #variables outcome 1 predictor 5 Operations: PLS feature extraction with all_predictors() --- Code print(rec) Output Recipe Inputs: role #variables outcome 1 predictor 5 Training data contained 456 data points and no missing data. Operations: PLS feature extraction with carbon, hydrogen, oxygen, nitrogen, sulfur [trained] # can prep recipes with no keep_original_cols Code pls_trained <- prep(pls_rec, training = biom_tr, verbose = FALSE) Condition Warning: 'keep_original_cols' was added to `step_pls()` after this recipe was created. Regenerate your recipe to avoid this warning. # empty printing Code rec Output Recipe Inputs: role #variables outcome 1 predictor 10 Operations: PLS feature extraction with <none> --- Code rec Output Recipe Inputs: role #variables outcome 1 predictor 10 Training data contained 32 data points and no missing data. Operations: PLS feature extraction with <none> [trained]
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