step_sbf: Variable Selection by Filtering
In MachineShop: Machine Learning Models and Tools

step_sbf

R Documentation

Variable Selection by Filtering

Description

Creates a specification of a recipe step that will select variables from a candidate set according to a user-specified filtering function.

Usage

step_sbf(
  recipe,
  ...,
  filter,
  multivariate = FALSE,
  options = list(),
  replace = TRUE,
  prefix = "SBF",
  role = "predictor",
  skip = FALSE,
  id = recipes::rand_id("sbf")
)

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

Arguments

`recipe`	recipe object to which the step will be added.
`...`	one or more selector functions to choose which variables will be used to compute the components. See `selections` for more details. These are not currently used by the `tidy` method.
`filter`	function whose first argument `x` is a univariate vector or a `multivariate` data frame of candidate variables from which to select, second argument `y` is the response variable as defined in preceding recipe steps, and third argument `step` is the current step. The function should return a logical value or vector of length equal the number of variables in `x` indicating whether to select the corresponding variable, or return a list or data frame with element `selected` containing the logical(s) and possibly with other elements of the same length to be included in output from the `tidy` method.
`multivariate`	logical indicating that candidate variables be passed to the `x` argument of the `filter` function separately as univariate vectors if `FALSE`, or altogether in one multivariate data frame if `TRUE`.
`options`	list of elements to be added to the step object for use in the `filter` function.
`replace`	logical indicating whether to replace the original variables.
`prefix`	if the original variables are not replaced, the selected variables are added to the dataset with the character string prefix added to their names; otherwise, the original variable names are retained.
`role`	analysis role that added step variables should be assigned. By default, they are designated as model predictors.
`skip`	logical indicating whether to skip the step when the recipe is baked. While all operations are baked when `prep` is run, some operations may not be applicable to new data (e.g. processing outcome variables). Care should be taken when using `skip = TRUE` as it may affect the computations for subsequent operations.
`id`	unique character string to identify the step.
`x`	`step_sbf` object.

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 (selectors or variables selected), selected (logical indicator of selected variables), and name of the selected variable names.

Examples

library(recipes)

glm_filter <- function(x, y, step) {
  model_fit <- glm(y ~ ., data = data.frame(y, x))
  p_value <- drop1(model_fit, test = "F")[-1, "Pr(>F)"]
  p_value < step$threshold
}

rec <- recipe(rating ~ ., data = attitude)
sbf_rec <- rec %>%
  step_sbf(all_numeric_predictors(),
           filter = glm_filter, options = list(threshold = 0.05))

sbf_prep <- prep(sbf_rec, training = attitude)
sbf_data <- bake(sbf_prep, attitude)

pairs(sbf_data, lower.panel = NULL)

tidy(sbf_rec, number = 1)
tidy(sbf_prep, number = 1)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.