pls.md
In recipes: Preprocessing and Feature Engineering Steps for Modeling

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.

check_name() is used

Code
  prep(rec, training = dat)
Condition
  Error in `step_pls()`:
  Caused by error in `bake()`:
  ! Name collision occured. The following variable names already exists:
  i  PLS1

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.

empty printing

Code
  rec
Message

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

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

  -- Operations 
  * PLS feature extraction with: <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 
  * PLS feature extraction with: <none> | Trained

keep_original_cols - can prep recipes with it missing

Code
  rec <- prep(rec)
Condition
  Warning:
  'keep_original_cols' was added to `step_pls()` after this recipe was created.
  Regenerate your recipe to avoid this warning.

printing

Code
  print(rec)
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 5

  -- Operations 
  * PLS feature extraction with: all_predictors()

Code
  prep(rec)
Message

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

  -- Inputs 
  Number of variables by role
  outcome:   1
  predictor: 5

  -- Training information 
  Training data contained 456 data points and no incomplete rows.

  -- Operations 
  * PLS feature extraction with: carbon, hydrogen, oxygen, ... | Trained