validate_outcomes_are_binary: Ensure that the outcome has binary factors

View source: R/validation.R

validate_outcomes_are_binaryR Documentation

Ensure that the outcome has binary factors


validate - asserts the following:

  • outcomes must have binary factor columns.

check - returns the following:

  • ok A logical. Does the check pass?

  • bad_cols A character vector. The names of the columns with problems.

  • num_levels An integer vector. The actual number of levels of the columns with problems.






An object to check.


The expected way to use this validation function is to supply it the ⁠$outcomes⁠ element of the result of a call to mold().


validate_outcomes_are_binary() returns outcomes invisibly.

check_outcomes_are_binary() returns a named list of three components, ok, bad_cols, and num_levels.


hardhat provides validation functions at two levels.

  • ⁠check_*()⁠: check a condition, and return a list. The list always contains at least one element, ok, a logical that specifies if the check passed. Each check also has check specific elements in the returned list that can be used to construct meaningful error messages.

  • ⁠validate_*()⁠: check a condition, and error if it does not pass. These functions call their corresponding check function, and then provide a default error message. If you, as a developer, want a different error message, then call the ⁠check_*()⁠ function yourself, and provide your own validation function.

See Also

Other validation functions: validate_column_names(), validate_no_formula_duplication(), validate_outcomes_are_factors(), validate_outcomes_are_numeric(), validate_outcomes_are_univariate(), validate_prediction_size(), validate_predictors_are_numeric()


# Not a binary factor. 0 levels
check_outcomes_are_binary(data.frame(x = 1))

# Not a binary factor. 1 level
check_outcomes_are_binary(data.frame(x = factor("A")))

# All good
check_outcomes_are_binary(data.frame(x = factor(c("A", "B"))))

hardhat documentation built on March 31, 2023, 10:21 p.m.