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Ensure predictors are all numeric

Source: R/validation.R
validate_predictors_are_numeric.Rd

validate - asserts the following:

  • predictors must have numeric 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_predictors_are_numeric(predictors)

check_predictors_are_numeric(predictors, ..., call = caller_env())

Arguments

predictors

An object to check.

...

These dots are for future extensions and must be empty.

call

The call used for errors and warnings.

Value

validate_predictors_are_numeric() returns predictors invisibly.

check_predictors_are_numeric() 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 $predictors 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_outcomes_are_univariate(), validate_prediction_size()

Examples

# All good
check_predictors_are_numeric(mtcars)
#> $ok
#> [1] TRUE
#> 
#> $bad_classes
#> list()
#> 

# Species is not numeric
check_predictors_are_numeric(iris)
#> $ok
#> [1] FALSE
#> 
#> $bad_classes
#> $bad_classes$Species
#> [1] "factor"
#> 
#> 

# This gives an intelligent error message
try(validate_predictors_are_numeric(iris))
#> Error in validate_predictors_are_numeric(iris) : 
#>   All predictors must be numeric.
#> ℹ The following is not:
#> "Species": <factor>