Ensure that the outcome has binary factors
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)
outcomes |
An object to check. |
The expected way to use this validation function is to supply it the
$outcomes element of the result of a call to mold().
validate_outcomes_are_binary() returns outcomes invisibly.
check_outcomes_are_binary() returns a named list of three components,
ok, bad_cols, and num_levels.
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.
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()
# 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"))))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.