collinear (version 1.1.1)

validate_predictors: Validate the 'predictors' argument for analysis

Description

Requires the argument 'df' to be validated with validate_df().

Validates the 'predictors' argument to ensure it complies with the requirements of the package functions. It performs the following actions:

• Stops if 'df' is NULL.

• Stops if 'df' is not validated.

• If 'predictors' is NULL, uses column names of 'df' as 'predictors' in the 'df' data frame.

• Raise a warning if there are names in 'predictors' not in the column names of 'df', and returns only the ones in 'df'.

• Stop if the number of numeric columns in 'predictors' is smaller than 'min_numerics'.

• Raise a warning if there are zero-variance columns in 'predictors' and returns a new 'predictors' argument without them.

• Tags the vector with the attribute validated = TRUE to let the package functions skip the data validation.

Usage

validate_predictors(
  df = NULL,
  response = NULL,
  predictors = NULL,
  min_numerics = 0,
  decimals = 4
)

Value

A character vector of validated predictor names

Arguments

df

(required; data frame) A validated data frame with numeric and/or character predictors, and optionally, a response variable. Default: NULL.

response

(optional, character string) Name of a numeric response variable. Used to remove the response from the predictors when predictors is NULL. Character response variables are ignored. Default: NULL.

predictors

(optional; character vector) character vector with predictor names in 'df'. If omitted, all columns of 'df' are used as predictors. Default:NULL

min_numerics

(required, integer) Minimum number of numeric predictors required. Default: 1

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

Author

Blas M. Benito

Examples

data(
  vi,
  vi_predictors
  )

#validating example data frame
vi <- validate_df(
  df = vi
)

#validating example predictors
vi_predictors <- validate_predictors(
  df = vi,
  predictors = vi_predictors
)

#tagged as validated
attributes(vi_predictors)$validated