validate - asserts the following:

• outcomes must have 1 column. Atomic vectors are treated as 1 column matrices.

check - returns the following:

• ok A logical. Does the check pass?

• n_cols A single numeric. The actual number of columns.

## Usage

validate_outcomes_are_univariate(outcomes)

check_outcomes_are_univariate(outcomes)

## Arguments

outcomes

An object to check.

## Value

validate_outcomes_are_univariate() returns outcomes invisibly. check_outcomes_are_univariate() returns a named list of two components, ok and n_cols.

The expected way to use this validation function is to supply it the $outcomes element of the result of a call to mold(). ## Validation hardhat provides validation functions at two levels. • check_*(): check a condition, and return a list. The list always contains at least one element, ok, a logical that specifies if the check passed. Each check also has check specific elements in the returned list that can be used to construct meaningful error messages. • validate_*(): check a condition, and error if it does not pass. These functions call their corresponding check function, and then provide a default error message. If you, as a developer, want a different error message, then call the check_*() function yourself, and provide your own validation function. ## See also Other validation functions: validate_column_names(), validate_no_formula_duplication(), validate_outcomes_are_binary(), validate_outcomes_are_factors(), validate_outcomes_are_numeric(), validate_prediction_size(), validate_predictors_are_numeric() ## Examples validate_outcomes_are_univariate(data.frame(x = 1)) try(validate_outcomes_are_univariate(mtcars)) #> Error in glubort("The outcome must be univariate, but {check$n_cols} columns were found.") :
#>   The outcome must be univariate, but 11 columns were found.