step_pls: 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,
  predictor_prop = 1,
  outcome = NULL,
  options = list(scale = TRUE),
  preserve = deprecated(),
  res = NULL,
  columns = NULL,
  prefix = "PLS",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("pls")
)

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

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 variables for this step. See selections() for more details.
role: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from 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 components to retain as new predictors. If num_comp is greater than the number of columns or the number of possible components, a smaller value will be used. If num_comp = 0 is set then no transformation is done and selected variables will stay unchanged, regardless of the value of keep_original_cols.
predictor_prop: The maximum number of original predictors that can have non-zero coefficients for each PLS component (via regularization).
outcome: When a single outcome is available, character string or call to dplyr::vars() can be used to specify a single outcome variable.
options: A list of options to mixOmics::pls(), mixOmics::spls(), mixOmics::plsda(), or mixOmics::splsda() (depending on the data and arguments).
preserve: Use keep_original_cols instead to specify whether the original predictor data should be retained along with the new features.
res: A list of results are stored here once this preprocessing step has been trained by prep().
columns: A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.
prefix: A character string for the prefix of the resulting new variables. See notes below.
keep_original_cols: A logical to keep the original variables in the output. Defaults to FALSE.
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, component , and id:

terms: character, the selectors or variables selected
value: numeric, coefficients defined as \(W(P'W)^{-1}\)
size: character, name of component
id: character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

num_comp: # Components (type: integer, default: 2)
predictor_prop: Proportion of Predictors (type: double, default: 1)

Case weights

The underlying operation does not allow for case weights.

Details

PLS is a supervised version of principal component analysis that requires the outcome data to compute the new features.

This step requires the Bioconductor mixOmics package. If not installed, the step will stop with a note about installing the package. Install mixOmics using the pak package:

# install.packages("pak")
pak::pak("mixOmics")

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 PLS1 - PLS101.

Sparsity can be encouraged using the predictor_prop parameter. This affects each PLS component, and indicates the maximum proportion of predictors with non-zero coefficients in each component. step_pls() converts this proportion to determine the keepX parameter in mixOmics::spls() and mixOmics::splsda(). See the references in mixOmics::spls() for details.

References

https://en.wikipedia.org/wiki/Partial_least_squares_regression

Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) mixOmics: An R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752. tools:::Rd_expr_doi("10.1371/journal.pcbi.1005752")

Examples

Run this code

# requires the Bioconductor mixOmics package
data(biomass, package = "modeldata")

biom_tr <-
  biomass %>%
  dplyr::filter(dataset == "Training") %>%
  dplyr::select(-dataset, -sample)
biom_te <-
  biomass %>%
  dplyr::filter(dataset == "Testing") %>%
  dplyr::select(-dataset, -sample, -HHV)

dense_pls <-
  recipe(HHV ~ ., data = biom_tr) %>%
  step_pls(all_numeric_predictors(), outcome = "HHV", num_comp = 3)

sparse_pls <-
  recipe(HHV ~ ., data = biom_tr) %>%
  step_pls(all_numeric_predictors(), outcome = "HHV", num_comp = 3,
           predictor_prop = 4 / 5)

## -----------------------------------------------------------------------------
## PLS discriminant analysis

data(cells, package = "modeldata")

cell_tr <-
  cells %>%
  dplyr::filter(case == "Train") %>%
  dplyr::select(-case)
cell_te <-
  cells %>%
  dplyr::filter(case == "Test") %>%
  dplyr::select(-case, -class)

dense_plsda <-
  recipe(class ~ ., data = cell_tr) %>%
  step_pls(all_numeric_predictors(), outcome = "class", num_comp = 5)

sparse_plsda <-
  recipe(class ~ ., data = cell_tr) %>%
  step_pls(all_numeric_predictors(), outcome = "class", num_comp = 5,
           predictor_prop = 1 / 4)

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