Find near-zero variance variables in a dataframe

Returns the names of near-zero variance variables in a modelling dataframe.

Usage

identify_zero_variance_variables(
  df = NULL,
  responses = NULL,
  predictors = NULL,
  decimals = 4,
  quiet = FALSE,
  ...
)

Arguments

df

(required; dataframe, tibble, or sf) A dataframe with responses (optional) and predictors. Must have at least 10 rows for pairwise correlation analysis, and 10 * (length(predictors) - 1) for VIF. Default: NULL.

responses

(optional; character, character vector, or NULL) Name of one or several response variables in df. Default: NULL.

predictors

(optional; character vector or NULL) Names of the predictors in df. If NULL, all columns except responses and constant/near-zero-variance columns are used. Default: NULL.

decimals

(required, integer) Number of decimal places for the zero variance test. Smaller numbers will increase the number of variables detected as near-zero variance. Recommended values will depend on the range of the numeric variables in 'df'. Default: 4

quiet

(optional; logical) If FALSE, messages are printed. Default: FALSE.

...

(optional) Internal args (e.g. function_name for validate_arg_function_name, a precomputed correlation matrix m, or cross-validation args for preference_order).

Value

character vector: names of near-zero variance columns.

See also

Other data_types: identify_categorical_variables(), identify_logical_variables(), identify_numeric_variables(), identify_response_type(), identify_valid_variables()

Author

Blas M. Benito, PhD

Examples

data(vi_smol, vi_predictors)

#create zero and near variance predictors
vi_smol$zero_variance <- 1
vi_smol$near_zero_variance <- runif(
  n = nrow(vi_smol),
  min = 0,
  max = 0.0001
  )


#add to vi predictors
vi_predictors <- c(
  vi_predictors,
  "zero_variance",
  "near_zero_variance"
)

#identify zero variance predictors
x <- identify_zero_variance_variables(
  df = vi_smol,
  predictors = vi_predictors
)
#> 
#> collinear::identify_zero_variance_variables(): invalid predictors due to near-zero variance:
#>  - zero_variance
#>  - near_zero_variance

x
#> [1] "zero_variance"      "near_zero_variance"