validate - asserts the following:
outcomesmust have binary factor columns.
check - returns the following:
okA logical. Does the check pass?bad_colsA character vector. The names of the columns with problems.num_levelsAn integer vector. The actual number of levels of the columns with problems.
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 thecheck_*()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))
#> $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)
#>