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Check if an AVL dataframe satisfies assumptions of monotonicity.

Source: R/tides_validation.R
validate_monotonicity.Rd

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

  • Weak monotonicity, either flat or increasing

  • Strict monotonicty, increasing only

  • Speeds satisfy Fritsch-Carlson constraints

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

return_full

Optional. Should a dataframe of each point checked be returned? Default is FALSE. for slopes be checked? Default is FALSE, where the speed check will return NA.

Value

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

Examples

# Get & test non-monotonic dataset
c53_dists <- new_transittraj_data("get_linear_distances")
c53_dists_val <- validate_monotonicity(c53_dists, check_speed = TRUE)
print(c53_dists_val)
#>   weak strict  speed 
#>  FALSE  FALSE  FALSE 

# Get & test monotonic dataset
c53_mono <- new_transittraj_data("make_monotonic")
c53_mono_val <- validate_monotonicity(c53_mono, check_speed = TRUE)
print(c53_mono_val)
#>   weak strict  speed 
#>   TRUE   TRUE   TRUE