tests/testthat/_snaps/pls.md

In tidymodels/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()`:
  ! Only multivariate models for numeric outcomes are supports.

PLS-DA, sparse loadings, multiple outcomes

Code
  prep(rec)
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! Only multivariate models for numeric outcomes are supports.

check_name() is used

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

rethrows error correctly from implementation

Code
  tmp <- recipe(~., data = mtcars) %>% step_pls(all_predictors(), outcome = mpg) %>%
    prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! Failed to compute:
  Caused by error in `mixOmics::pls()`:
  ! mocked error

error on no outcome

Code
  recipe(~., data = mtcars) %>% step_pls(all_predictors()) %>% prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `outcome` must not be `NULL`.

check_options() is used

Code
  recipe(~., data = mtcars) %>% step_pls(disp, outcome = mpg, options = TRUE) %>%
    prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `options` must be a list, not `TRUE`.

recipes_argument_select() is used

Code
  recipe(mpg ~ ., data = mtcars) %>% step_pls(disp, outcome = NULL) %>% prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `outcome` must not be `NULL`.

bake method errors when needed non-standard role columns are missing

Code
  bake(rec, new_data = biom_tr[, c(-1)])
Condition
  Error in `step_pls()`:
  ! The following required column is missing from `new_data`: carbon.

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.
  i 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

bad args

Code
  recipe(mpg ~ ., data = mtcars) %>% step_pls(-mpg, outcome = mpg, num_comp = -1) %>%
    prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `num_comp` must be a whole number larger than or equal to 0, not the number -1.

Code
  recipe(mpg ~ ., data = mtcars) %>% step_pls(-mpg, outcome = mpg, prefix = 1) %>%
    prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `prefix` must be a single string, not the number 1.

Code
  recipe(mpg ~ ., data = mtcars) %>% step_pls(-mpg, outcome = mpg,
    predictor_prop = -1) %>% prep()
Condition
  Error in `step_pls()`:
  Caused by error in `prep()`:
  ! `predictor_prop` must be a number between 0 and 1, not the number -1.

tidymodels/recipes documentation built on April 17, 2025, 6:17 p.m.