validate_outcomes_are_binary {hardhat}

Ensure that the outcome has binary factors

Description

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.

Usage

validate_outcomes_are_binary(outcomes)

check_outcomes_are_binary(outcomes)

Arguments

outcomes

An object to check.

Details

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

Value

validate_outcomes_are_binary() returns outcomes invisibly.

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

Validation

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()

Examples

# 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"))))