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

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

# S3 method for step_corr
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 are affected by the step. See selections() for more details. For the tidy method, these are not currently used.

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 stats::cor() function.

method

A character string for the method argument to the stats::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.

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_corr 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 which is the columns that will be removed.

Details

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

When a column has a single unique value, that column will be excluded from the correlation analysis. Also, if the data set has sporadic missing values (and an inappropriate value of use is chosen), some columns will also be excluded from the filter.

See also

Author

Original R code for filtering algorithm by Dong Li, modified by Max Kuhn. Contributions by Reynald Lescarbeau (for original in caret package). Max Kuhn for the step function.

Examples

library(modeldata) 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)
#> carbon hydrogen oxygen nitrogen sulfur duplicate #> carbon 1.00 0.32 0.63 0.15 0.09 1.00 #> hydrogen 0.32 1.00 0.54 0.07 0.19 0.31 #> oxygen 0.63 0.54 1.00 0.18 0.31 0.63 #> nitrogen 0.15 0.07 0.18 1.00 0.27 0.15 #> sulfur 0.09 0.19 0.31 0.27 1.00 0.10 #> duplicate 1.00 0.31 0.63 0.15 0.10 1.00
round(abs(cor(filtered_te)), 2)
#> hydrogen nitrogen sulfur duplicate HHV #> hydrogen 1.00 0.11 0.26 0.20 0.10 #> nitrogen 0.11 1.00 0.16 0.13 0.11 #> sulfur 0.26 0.16 1.00 0.13 0.08 #> duplicate 0.20 0.13 0.13 1.00 0.94 #> HHV 0.10 0.11 0.08 0.94 1.00
tidy(corr_filter, number = 1)
#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 NA corr_ubc7G
tidy(filter_obj, number = 1)
#> # A tibble: 2 x 2 #> terms id #> <chr> <chr> #> 1 oxygen corr_ubc7G #> 2 carbon corr_ubc7G