step_pca_sparse {embed}

Sparse PCA Signal Extraction

Description

step_pca_sparse() creates a specification of a recipe step that will convert numeric data into one or more principal components that can have some zero coefficients.

Usage

step_pca_sparse(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_comp = 5,
  predictor_prop = 1,
  options = list(),
  res = NULL,
  prefix = "PC",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("pca_sparse")
)

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 components. 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 principal component 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 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 PCA component (via regularization).

options

A list of options to the default method for irlba::ssvd().

res

The rotation matrix once this preprocessing step has be trained by prep().

prefix

A character string that will be the prefix to 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 recipes::bake()? While all operations are baked when recipes::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.

Details

The irlba package is required for this step. If it is not installed, the user will be prompted to do so when the step is defined. The irlba::ssvd() function is used to encourage sparsity; that documentation has details about this method.

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 PC1 - PC9. If num_comp = 101, the names would be PC1 - PC101.

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), value (the loading), and component.

Tidying

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

terms

character, the selectors or variables selected

value

numeric, variable loading

component

character, principle component

id

character, id of this step

Tuning Parameters

This step has 2 tuning parameters:

• num_comp: # Components (type: integer, default: 5)

• predictor_prop: Proportion of Predictors (type: double, default: 1)

Case weights

The underlying operation does not allow for case weights.

See Also

step_pca_sparse_bayes()

Examples

library(recipes)
library(ggplot2)

data(ad_data, package = "modeldata")

ad_rec <-
  recipe(Class ~ ., data = ad_data) %>%
  step_zv(all_predictors()) %>%
  step_YeoJohnson(all_numeric_predictors()) %>%
  step_normalize(all_numeric_predictors()) %>%
  step_pca_sparse(
    all_numeric_predictors(),
    predictor_prop = 0.75,
    num_comp = 3,
    id = "sparse pca"
  ) %>%
  prep()

tidy(ad_rec, id = "sparse pca") %>%
  mutate(value = ifelse(value == 0, NA, value)) %>%
  ggplot(aes(x = component, y = terms, fill = value)) +
  geom_tile() +
  scale_fill_gradient2() +
  theme(axis.text.y = element_blank())