Ensure that the outcome has only factor columns

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)

Arguments

outcomes

An object to check.

Value

validate_outcomes_are_factors() returns outcomes invisibly.

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

Details

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

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_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))
#> $ok
#> [1] FALSE
#> 
#> $bad_classes
#> $bad_classes$x
#> [1] "numeric"
#> 
#> 

# All good
check_outcomes_are_factors(data.frame(x = factor(c("A", "B"))))
#> $ok
#> [1] TRUE
#> 
#> $bad_classes
#> list()
#>