hardhat (version 1.4.2)

validate_outcomes_are_factors: Ensure that the outcome has only factor columns

Description

validate - asserts the following:

• outcomes must have factor columns.

check - returns the following:

• ok A logical. Does the check pass?

• bad_classes A named list. The names are the names of problematic columns, and the values are the classes of the matching column.

Usage

validate_outcomes_are_factors(outcomes)
check_outcomes_are_factors(outcomes, ..., call = caller_env())

Value

validate_outcomes_are_factors() returns outcomes invisibly.

check_outcomes_are_factors() returns a named list of two components, ok and bad_classes.

Arguments

outcomes

An object to check.

...

These dots are for future extensions and must be empty.

call

The call used for errors and warnings.

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.

Details

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

See Also

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

Examples

# Not a factor column.
check_outcomes_are_factors(data.frame(x = 1))

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