bayespecon.models.flow_panel.SARFlowPanel¶

class bayespecon.models.flow_panel.SARFlowPanel(y, G, X, T, col_names=None, k=None, model=0, priors=None, logdet_method='traces', restrict_positive=True, robust=False, miter=30, titer=800, trace_riter=50, trace_seed=None, symmetric_xo_xd=None)[source]¶

Panel spatial-lag origin-destination flow model with unrestricted dependence.

For each period \(t\), the vectorized flow matrix \(y_t \in \mathbb{R}^{N}\) with \(N = n^2\) satisfies

\[y_t = \rho_d W_d y_t + \rho_o W_o y_t + \rho_w W_w y_t + X_t \beta + \varepsilon_t, \qquad \varepsilon_t \sim \mathcal{N}(0, \sigma^2 I_N).\]

The panel stack is time-first across \(T\) periods. The model argument controls pooled, pair fixed-effects, time fixed-effects, or two-way demeaning before the likelihood is evaluated. The Jacobian contribution scales as \(T \log |A(\rho_d, \rho_o, \rho_w)|\).

__init__(y, G, X, T, col_names=None, k=None, model=0, priors=None, logdet_method='traces', restrict_positive=True, robust=False, miter=30, titer=800, trace_riter=50, trace_seed=None, symmetric_xo_xd=None)[source]¶

Methods

__init__(y, G, X, T[, col_names, k, model, ...])
fit([draws, tune, chains, target_accept, ...])	Draw samples from the posterior.
fit_approx([draws, n, method, random_seed, ...])	Fit a variational approximation and return posterior draws.
posterior_predictive([n_draws, random_seed])	Draw posterior-predictive samples y_rep for the full panel stack.
spatial_effects([draws, ...])	Summarise posterior origin/destination/intra/network/total effects.
summary([var_names])	Return posterior summary table via ArviZ.

Attributes

approximation	Return the most recent PyMC variational approximation, if any.
inference_data	Return posterior draws from the most recent fit.
pymc_model	Return the most recently built PyMC model.

property approximation[source]¶

Return the most recent PyMC variational approximation, if any.

fit(draws=2000, tune=1000, chains=4, target_accept=0.9, random_seed=None, store_lambda=False, idata_kwargs=None, **sample_kwargs)[source]¶

Draw samples from the posterior.

fit_approx(draws=2000, n=10000, method='advi', random_seed=None, store_lambda=False, compute_log_likelihood=True, **fit_kwargs)[source]¶

Fit a variational approximation and return posterior draws.

property inference_data : arviz.data.inference_data.InferenceData | None[source]¶

Return posterior draws from the most recent fit.

posterior_predictive(n_draws=None, random_seed=None)[source]¶

Draw posterior-predictive samples y_rep for the full panel stack.

Parameters:¶

n_draws : int, optional¶

Number of posterior draws to use. Defaults to all.

random_seed : int, optional¶

Seed for the noise/Poisson sampler.

Returns:¶

Array of shape (n_draws, N_flow * T) with posterior-predictive flows in time-first stacked order.

Return type:¶

np.ndarray

property pymc_model : pymc.model.core.Model | None[source]¶

Return the most recently built PyMC model.

spatial_effects(draws=None, return_posterior_samples=False, ci=0.95, mode='auto')[source]¶

Summarise posterior origin/destination/intra/network/total effects.

See bayespecon.models.flow.FlowModel.spatial_effects() for the mode semantics (auto / combined / separate destination-origin sides per Thomas-Agnan & LeSage 2014, §83.5.2).

summary(var_names=None, **kwargs)[source]¶

Return posterior summary table via ArviZ.