VAR-LiNGAM

class lingam.VARLiNGAM(lags=1, criterion='bic', prune=True, ar_coefs=None, lingam_model=None, random_state=None)

Implementation of VAR-LiNGAM Algorithm [1]

References

[1]

Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer. Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. Journal of Machine Learning Research, 11: 1709-1731, 2010.

__init__(lags=1, criterion='bic', prune=True, ar_coefs=None, lingam_model=None, random_state=None)

Construct a VARLiNGAM model.

Parameters:

• lags (int, optional (default=1)) – Number of lags.

• criterion ({‘aic’, ‘fpe’, ‘hqic’, ‘bic’, None}, optional (default='bic')) – Criterion to decide the best lags within lags. Searching the best lags is disabled if criterion is None.

• prune (boolean, optional (default=True)) – Whether to prune the adjacency matrix of lags.

• ar_coefs (array-like, optional (default=None)) – Coefficients of AR model. Estimating AR model is skipped if specified ar_coefs. Shape must be (lags, n_features, n_features).

• lingam_model (lingam object inherits 'lingam._BaseLiNGAM', optional (default=None)) – LiNGAM model for causal discovery. If None, DirectLiNGAM algorithm is selected.

• random_state (int, optional (default=None)) – random_state is the seed used by the random number generator.

property adjacency_matrices_

Estimated adjacency matrix.

Returns:

adjacency_matrices_ – The adjacency matrix of fitted model, where n_features is the number of features.

Return type:

array-like, shape (lags, n_features, n_features)

bootstrap(X, n_sampling)

Evaluate the statistical reliability of DAG based on the bootstrapping.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• n_sampling (int) – Number of bootstrapping samples.

Returns:

result – Returns the result of bootstrapping.

Return type:

TimeseriesBootstrapResult

property causal_order_

Estimated causal ordering.

Returns:

causal_order_ – The causal order of fitted model, where n_features is the number of features.

Return type:

array-like, shape (n_features)

estimate_total_effect(X, from_index, to_index, from_lag=0)

Estimate total effect using causal model.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Original data, where n_samples is the number of samples and n_features is the number of features.

• from_index – Index of source variable to estimate total effect.

• to_index – Index of destination variable to estimate total effect.

Returns:

total_effect – Estimated total effect.

Return type:

float

estimate_total_effect2(n_features, from_index, to_index, from_lag=0)

Estimate total effect using causal model.

Parameters:

• n_features – The number of features.

• from_index – Index of source variable to estimate total effect.

• to_index – Index of destination variable to estimate total effect.

Returns:

total_effect – Estimated total effect.

Return type:

float

fit(X)

Fit the model to X.

Parameters:

X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

Returns:

self – Returns the instance itself.

Return type:

object

get_error_independence_p_values()

Calculate the p-value matrix of independence between error variables.

Returns:

independence_p_values – p-value matrix of independence between error variables.

Return type:

array-like, shape (n_features, n_features)

property residuals_

Residuals of regression.

Returns:

residuals_ – Residuals of regression, where n_samples is the number of samples.

Return type:

array-like, shape (n_samples)