Calculate interval‐based validation metrics—including distance, likelihood, and energy‐score metrics—for all transition pairs in a BirdFlowIntervals object.
Value
A list with two elements:
- metrics
A named numeric vector of summary metrics across all intervals:
- mean_pred
Mean weighted average distance (km) from BF predictions
- mean_st
Mean weighted average distance (km) from S&T distributions
- mean_win_prob
Mean win probability (BF vs. S&T)
- mean_win_distance
Mean absolute distance improvement (km)
- mean_win_distance_fraction
Mean normalized distance improvement
- mean_global_prob_of_the_starting
Mean relative abundance at start cells
- mean_elapsed_days
Mean elapsed days per interval
- mean_elapsed_km
Mean observed great‐circle distance (km)
- mean_null_ll
Mean log‐likelihood under the S&T null distribution
- mean_ll
Mean log‐likelihood under the BF prediction
- mean_energy_score_bf
Mean energy score of BF predictions
- mean_energy_score_st
Mean energy score of S&T distributions
- mean_energy_improvement
Mean difference in energy score
- mean_pred_elapsed_dist_by_pred
Mean predicted elapsed distance by BF
- mean_pred_elapsed_dist_by_st
Mean predicted elapsed distance by S&T
- weighted_mean_win_prob
Global‐abundance‐weighted mean win probability
- weighted_mean_win_distance
Global‐abundance‐weighted mean win distance
- weighted_mean_win_distance_fraction
Global‐abundance‐weighted mean distance fraction
- weighted_mean_null_ll
Global‐abundance‐weighted mean null log‐likelihood
- weighted_mean_ll
Global‐abundance‐weighted mean log‐likelihood
- weighted_energy_improvement
Global‐abundance‐weighted mean energy improvement
- n_intervals
Number of transition pairs evaluated
- per_interval
A
data.frameof the raw, per‐transition metrics (same fields as above without the “mean_” prefix)
Examples
route_df <- data.frame(
route_id = c("001", "001", "001", "001", "001", "003", "003", "003", "004"),
date = as.Date(c("2025-01-01", "2025-01-08", "2025-01-15", "2025-01-21",
"2025-02-10", "2025-03-01", "2025-05-01", "2025-06-01",
"2025-05-01")),
lon = c(-75.0060, -75.0060, -74.0060, -87.6298, -87.6298, -87.6298,
-89.6298, -85.6298, -95.3698),
lat = c(39.7128, 39.7128, 40.7128, 41.8781, 41.8781, 41.8781,
42.8781, 40.8781, 29.7604),
route_type = c("tracking", "tracking", "tracking", "tracking",
"tracking", "motus", "motus", "motus", "motus")
)
bf <- BirdFlowModels::amewoo
species1 <- bf$species
source1 <- "Testing"
my_routes <- Routes(route_df,
species = species1,
source = source1
)
my_bfroutes <- as_BirdFlowRoutes(my_routes, bf = bf)
# Constraints
min_day <- 7
max_day <- 180
min_km <- 200
max_km <- 8000
my_intervals <- as_BirdFlowIntervals(my_bfroutes,
max_n = 1000,
min_day_interval = min_day,
max_day_interval = max_day,
min_km_interval = min_km,
max_km_interval = max_km
)
eval_res <- calc_interval_metrics(my_intervals, bf)
single_value_outputs <- eval_res[[1]]
transition_level_outputs <- eval_res[[2]]