step_pls {recipes} | R Documentation

## Partial Least Squares Feature Extraction

### Description

`step_pls` creates a specification of a recipe step that will convert numeric data into one or more new dimensions.

### Usage

    step_pls(recipe, ..., role = "predictor", trained = FALSE,
      num_comp = 2, outcome = NULL, options = NULL, res = NULL,
      num = NULL, prefix = "PLS", skip = FALSE, id = rand_id("pls"))

    ## S3 method for class 'step_pls'
    tidy(x, ...)

### Arguments

`recipe` | A recipe object. The step will be added to the sequence of operations for this recipe.
`...` | One or more selector functions to choose which variables will be used to compute the dimensions. See `selections()` for more details. For the `tidy` method, these are not currently used.
`role` | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new dimension columns created by the original variables will be used as predictors in a model.
`trained` | A logical to indicate if the quantities for preprocessing have been estimated.
`num_comp` | The number of pls dimensions to retain as new predictors. If `num_comp` is greater than the number of columns or the number of possible dimensions, a smaller value will be used.
`outcome` | When a single outcome is available, character string or call to `dplyr::vars()` can be used to specify the variable. When there are multipole outcomes, `dplyr::vars()` must be used. This that can include specific variable names separated by commas or different selectors (see `selections()`).
`options` | A list of options to `pls::plsr()`.
`res` | The `pls::plsr()` object is stored here once this preprocessing step has be trained by `prep.recipe()`.
`num` | The number of components to retain (this will be deprecated in factor of `num_comp` in version 0.1.5). `num_comp` will override this option.
`prefix` | A character string that will be the prefix to the resulting new variables. See notes below.
`skip` | A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations
`id` | A character string that is unique to this step to identify it.
`x` | A `step_pls` object

### Details

PLS is a supervised version of principal component analysis that requires one or more numeric outcomes to compute the new features. The data should be scaled (and perhaps centered) prior to running these calculations.

This step requires the pls package. If not installed, the step will stop with a note about installing the package.

The argument `num_comp` controls the number of components that will be retained (the original variables that are used to derive the components are removed from the data). The new components will have names that begin with `prefix` and a sequence of numbers. The variable names are padded with zeros. For example, if `num_comp < 10`, their names will be `PLS1` - `PLS9`. If `num_comp = 101`, the names would be `PLS001` - `PLS101`.

### Value

An updated version of `recipe` with the new step added to the sequence of existing steps (if any). For the `tidy` method, a tibble with columns `terms` (the selectors or variables selected).

### See Also

`step_pca()` `step_kpca()` `step_ica()` `recipe()` `prep.recipe()` `bake.recipe()`

### Examples

    data(biomass)

    biomass_tr <- biomass[biomass$dataset == "Training",]
    biomass_te <- biomass[biomass$dataset == "Testing",]

    pls_rec <- recipe(HHV ~ ., data = biomass_tr) %>%
      step_rm(sample, dataset) %>%
      step_center(all_predictors()) %>%
      step_scale(all_predictors()) %>%
      # If the outcome(s) need standardization, do it in separate
      # steps with skip = FALSE so that new data where the
      # outcome is missing can be processed.
      step_center(all_outcomes(), skip = TRUE) %>%
      step_scale(all_outcomes(), skip = TRUE) %>%
      step_pls(all_predictors(), outcome = "HHV")

    pls_rec <- prep(pls_rec, training = biomass_tr, retain = TRUE)

    pls_test_scores <- bake(pls_rec, new_data = biomass_te[, -8])

    tidy(pls_rec, number = 6)

[Package recipes version 0.1.5 Index]