Check if AVL data satisfies assumptions of monotonicity — validate

This function checks whether the provided AVL dataframe of linearized distances satisfies three conditions:

Weak monotonicity, either flat or increasing
Strict monotonicty, increasing only
Fritsch-Carlson constraints for speeds

See make_monotonic() for more information.

Usage

validate_monotonicity(distance_df, check_speed = FALSE, return_full = FALSE)

Arguments

distance_df: A dataframe of linearized AVL data. Must include trip_id_performed, event_timestamp, and distance. If check_speed = TRUE, must also include speed.
check_speed: Optional. A boolean, should the Fritsch-Carlson conditions for slopes be checked? Default is FALSE, where the speed check will return NA.
return_full: Optional. Should a dataframe of each point checked be returned? Default is FALSE.

Value

A named vector of booleans indicating whether each of the three conditions are satisfied.

Examples

# Get & test non-monotonic dataset
lineE_dists <- new_transittraj_data("get_linear_distances")
lineE_dists_val <- validate_monotonicity(lineE_dists, check_speed = TRUE)
print(lineE_dists_val)
#>   weak strict  speed 
#>  FALSE  FALSE  FALSE 

# Get & test monotonic dataset
lineE_mono <- new_transittraj_data("make_monotonic")
lineE_mono_val <- validate_monotonicity(lineE_mono, check_speed = TRUE)
print(lineE_mono_val)
#>   weak strict  speed 
#>   TRUE   TRUE   TRUE