combined_prob {QuAnTeTrack} | R Documentation |
Calculate combined probabilities of similarity or intersection metrics of tracks
Description
combined_prob()
calculates the combined probabilities of similarity and intersection metrics
derived from different models. The function uses simulation data to extract p-values, providing insight into
the significance of combined metrics across various similarity assessments.
Usage
combined_prob(data, metrics = NULL)
Arguments
data |
A
|
metrics |
A list of |
Details
The combined_prob()
function combines p-values derived from multiple similarity metric tests and intersection tests.
It calculates the combined p-values by assessing the probability of observing the combined metrics across simulated datasets.
This function is particularly useful for comparing multiple models and evaluating their collective performance in terms of p-values.
Value
A list containing:
P_values (model names) |
A matrix of p-values for the combined metrics across all trajectories. Each entry represents the probability of observing the combined metrics between the corresponding pair of trajectories. |
P_values_combined (model names) |
A numeric value representing the overall probability of observing the combined metrics, across all pairs of trajectories. |
Logo
Author(s)
Humberto G. Ferrón
humberto.ferron@uv.es
Macroevolution and Functional Morphology Research Group (www.macrofun.es)
Cavanilles Institute of Biodiversity and Evolutionary Biology
Calle Catedrático José Beltrán Martínez, nº 2
46980 Paterna - Valencia - Spain
Phone: +34 (9635) 44477
See Also
tps_to_track
, simulate_track
, track_intersection
, simil_DTW_metric
, simil_Frechet_metric
Examples
# Example 1: "Directed" model and similarity metrics.
s1 <- simulate_track(PaluxyRiver, nsim = 3, model = "Directed")
DTW1 <- simil_DTW_metric(PaluxyRiver, test = TRUE, sim = s1, superposition = "None")
Frechet1 <- simil_Frechet_metric(PaluxyRiver, test = TRUE, sim = s1, superposition = "None")
int1 <- track_intersection(PaluxyRiver, test = TRUE, H1 = "Lower", sim = s1,
origin.permutation = "None")
combined_prob(PaluxyRiver, metrics = list(DTW1, Frechet1, int1))
# Example 2: "Constrained" model and similarity metrics.
s2 <- simulate_track(PaluxyRiver, nsim = 3, model = "Constrained")
DTW2 <- simil_DTW_metric(PaluxyRiver, test = TRUE, sim = s2,
superposition = "None")
Frechet2 <- simil_Frechet_metric(PaluxyRiver, test = TRUE, sim = s2,
superposition = "None")
int2 <- track_intersection(PaluxyRiver, test = TRUE, H1 = "Lower", sim = s2,
origin.permutation = "Min.Box")
combined_prob(PaluxyRiver, metrics = list(DTW2, Frechet2, int2))
# Example 3: "Unconstrained" model and similarity metrics.
s3 <- simulate_track(PaluxyRiver, nsim = 3, model = "Unconstrained")
DTW3 <- simil_DTW_metric(PaluxyRiver, test = TRUE, sim = s3,
superposition = "None")
Frechet3 <- simil_Frechet_metric(PaluxyRiver, test = TRUE, sim = s3,
superposition = "None")
int3 <- track_intersection(PaluxyRiver, test = TRUE, H1 = "Lower", sim = s3,
origin.permutation = "Conv.Hull")
combined_prob(PaluxyRiver, metrics = list(DTW3, Frechet3, int3))