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.

validate_outcomes_are_binary(outcomes)

check_outcomes_are_binary(outcomes)

Arguments

outcomes

An object to check.

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.

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

Examples

# Not a binary factor. 0 levels check_outcomes_are_binary(data.frame(x = 1))
#> $ok #> [1] FALSE #> #> $bad_cols #> [1] "x" #> #> $num_levels #> [1] 0 #>
# Not a binary factor. 1 level check_outcomes_are_binary(data.frame(x = factor("A")))
#> $ok #> [1] FALSE #> #> $bad_cols #> [1] "x" #> #> $num_levels #> [1] 1 #>
# All good check_outcomes_are_binary(data.frame(x = factor(c("A", "B"))))
#> $ok #> [1] TRUE #> #> $bad_cols #> character(0) #> #> $num_levels #> integer(0) #>