recipes (version 0.1.0)

step_corr: High Correlation Filter

Description

step_corr creates a specification of a recipe step that will potentially remove variables that have large absolute correlations with other variables.

Usage

step_corr(recipe, ..., role = NA, trained = FALSE, threshold = 0.9,
  use = "pairwise.complete.obs", method = "pearson", removals = NULL)

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 are affected by the step. See selections for more details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

threshold

A value for the threshold of absolute correlation values. The step will try to remove the minimum number of columns so that all the resulting absolute correlations are less than this value.

use

A character string for the use argument to the cor function.

method

A character string for the method argument to the cor function.

removals

A character string that contains the names of columns that should be removed. These values are not determined until prep.recipe is called.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

This step attempts to remove variables to keep the largest absolute correlation between the variables less than threshold.

See Also

step_nzv recipe prep.recipe bake.recipe

Examples

# NOT RUN {
data(biomass)

set.seed(3535)
biomass$duplicate <- biomass$carbon + rnorm(nrow(biomass))

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

rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
                    sulfur + duplicate,
              data = biomass_tr)

corr_filter <- rec %>%
  step_corr(all_predictors(), threshold = .5)

filter_obj <- prep(corr_filter, training = biomass_tr)

filtered_te <- bake(filter_obj, biomass_te)
round(abs(cor(biomass_tr[, c(3:7, 9)])), 2)
round(abs(cor(filtered_te)), 2)
# }