LongitudinalBootstrapResult
- class lingam.LongitudinalBootstrapResult(n_timepoints, adjacency_matrices, total_effects)[source]
The result of bootstrapping for LongitudinalLiNGAM.
- __init__(n_timepoints, adjacency_matrices, total_effects)[source]
Construct a BootstrapResult.
- Parameters:
adjacency_matrices (array-like, shape (n_sampling)) – The adjacency matrix list by bootstrapping.
total_effects (array-like, shape (n_sampling)) – The total effects list by bootstrapping.
- property adjacency_matrices_
The adjacency matrix list by bootstrapping.
- Returns:
adjacency_matrices_ – The adjacency matrix list, where
n_sampling
is the number of bootstrap sampling.- Return type:
array-like, shape (n_sampling)
- get_causal_direction_counts(n_directions=None, min_causal_effect=None, split_by_causal_effect_sign=False)[source]
Get causal direction count as a result of bootstrapping.
- Parameters:
n_directions (int, optional (default=None)) – If int, then The top
n_directions
items are included in the resultmin_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.split_by_causal_effect_sign (boolean, optional (default=False)) – If True, then causal directions are split depending on the sign of the causal effect.
- Returns:
causal_direction_counts – List of causal directions sorted by count in descending order. The dictionary has the following format:
{'from': [n_directions], 'to': [n_directions], 'count': [n_directions]}
where
n_directions
is the number of causal directions.- Return type:
dict
- get_directed_acyclic_graph_counts(n_dags=None, min_causal_effect=None, split_by_causal_effect_sign=False)[source]
Get DAGs count as a result of bootstrapping.
- Parameters:
n_dags (int, optional (default=None)) – If int, then The top
n_dags
items are included in the resultmin_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.split_by_causal_effect_sign (boolean, optional (default=False)) – If True, then causal directions are split depending on the sign of the causal effect.
- Returns:
directed_acyclic_graph_counts – List of directed acyclic graphs sorted by count in descending order. The dictionary has the following format:
{'dag': [n_dags], 'count': [n_dags]}.
where
n_dags
is the number of directed acyclic graphs.- Return type:
dict
- get_paths(from_index, to_index, from_t, to_t, min_causal_effect=None)[source]
Get all paths from the start variable to the end variable and their bootstrap probabilities.
- Parameters:
from_index (int) – Index of the variable at the start of the path.
to_index (int) – Index of the variable at the end of the path.
from_t (int) – The starting timepoint of the path.
to_t (int) – The end timepoint of the path.
min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. Causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.
- Returns:
paths – List of path and bootstrap probability. The dictionary has the following format:
{'path': [n_paths], 'effect': [n_paths], 'probability': [n_paths]}
where
n_paths
is the number of paths.- Return type:
dict
- get_probabilities(min_causal_effect=None)[source]
Get bootstrap probability.
- Parameters:
min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.- Returns:
probabilities – List of bootstrap probability matrix.
- Return type:
array-like
- get_total_causal_effects(min_causal_effect=None)[source]
Get total effects list.
- Parameters:
min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.- Returns:
total_causal_effects – List of bootstrap total causal effect sorted by probability in descending order. The dictionary has the following format:
{'from': [n_directions], 'to': [n_directions], 'effect': [n_directions], 'probability': [n_directions]}
where
n_directions
is the number of causal directions.- Return type:
dict
- property total_effects_
The total effect list by bootstrapping.
- Returns:
total_effects_ – The total effect list, where
n_sampling
is the number of bootstrap sampling.- Return type:
array-like, shape (n_sampling)