neighbayes.models.OLSFlow¶

class neighbayes.models.OLSFlow(y, X, W, **kwargs)[source]¶

Non-spatial Bayesian OD-flow gravity model (independence baseline).

Implements the conventional log-linear gravity model from Thomas-Agnan and LeSage [2014] (eq. 83.2):

\[y = \alpha \iota_{N} + X_o \beta_o + X_d \beta_d + g\gamma + \varepsilon, \quad \varepsilon \sim \mathcal{N}(0, \sigma^{2} I_{N})\]

with no spatial-lag terms. Provided as a baseline for comparison with SARFlow / SARFlowSeparable and to reproduce Table 83.1 of the chapter.

Parameters:¶

y : array-like, shape (n, n) or (N,)¶

Observed O-D flow matrix (or its vec-form). Must be a square matrix or a flat vector of length \(N = n^2\).

W : libpysal.graph.Graph or scipy.sparse / dense (n×n) matrix¶

Row-standardized regional weights on n units (Graph or matrix). Required for API symmetry with the spatial flow models, but the graph weights are not used in estimation.

X : np.ndarray or pandas.DataFrame, shape (N, p)¶

Full origin-destination design matrix.

col_names : list[str], optional

Column labels for X. Defaults to ["x0", "x1", ...] when not provided and X is not a DataFrame.

k : int, optional

Number of regional attribute columns. Inferred from column names when they follow the dest_*/orig_* convention.

priors : dict, optional

Override default priors. Supported keys:

beta_mu : float, default 0.0 — Normal prior mean for beta.
beta_sigma : float, default 1e6 — Normal prior std for beta.
sigma_sigma : float, default 10.0 — HalfNormal prior std for sigma.

Spatial keys (rho_*) are ignored.

symmetric_xo_xd : bool, optional

If None (default), origin/destination design symmetry is auto-detected. Set explicitly to override.

Notes

No spatial-lag term enters the likelihood, so no log-determinant is required and logdet_method is ignored if passed.

__init__(y, X, W, **kwargs)[source]¶

Methods

`__init__`(y, X, W, **kwargs)
`fit`([draws, tune, chains, random_seed, ...])	Draw samples from the posterior via PyMC NUTS.
`fitted_values`()	Return fitted values at posterior mean parameters.
`posterior_predictive`([n_draws, random_seed])	Draw posterior-predictive flows for the OLS gravity model.
`residuals`()	Return residuals `y - fitted_values`.
`spatial_diagnostics`()	Run Bayesian LM specification tests and return a summary table.
`spatial_diagnostics_decision`([alpha, format])	Return a model-selection decision from Bayesian LM test results.
`spatial_effects`([draws, ...])	Summarize posterior origin/destination/intra/network/total effects.
`summary`([var_names])	Return posterior summary table.

Attributes

`inference_data`	Return the ArviZ InferenceData from the most recent fit.
`pymc_model`	Return the PyMC model object built for the most recent fit.

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

Draw samples from the posterior via PyMC NUTS.

Parameters:¶

draws : int, default 2000¶: Number of posterior samples per chain (after tuning).
tune : int, default 1000¶: Number of tuning (warm-up) steps per chain.
chains : int, default 4¶: Number of parallel chains.
random_seed : int, optional¶: Seed for reproducibility.
store_lambda : bool, default False¶: If True, include the high-dimensional fitted mean lambda in the stored posterior. Leaving this False reduces memory and conversion overhead for NB flow models.
idata_kwargs : dict, optional¶: Forwarded to pm.sample. {"log_likelihood": True} stores the pointwise log-likelihood that az.loo / az.waic / az.compare need; for SAR flow variants the captured Gaussian log-likelihood is post-processed to add the Jacobian contribution from log|I_N - rho_d W_d - rho_o W_o - rho_w W_w|. Off by default, as in PyMC: it holds one value per draw, chain, and flow.
progressbar : bool, default True¶: Show progress bar during sampling.
**sample_kwargs¶: Additional keyword arguments forwarded to pm.sample. Pass target_accept=0.95 to adjust the NUTS acceptance rate.

Return type:¶

arviz.InferenceData

fitted_values()[source]¶

Return fitted values at posterior mean parameters.

Returns:¶: Posterior-mean fitted values (on the model’s native scale; fixed-effects-transformed for panel models).
Return type:¶: np.ndarray

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

Return the ArviZ InferenceData from the most recent fit.

Returns:¶: The inference data object, or None if the model has not been fit yet.
Return type:¶: arviz.InferenceData or None

posterior_predictive(n_draws=None, random_seed=None)[source]¶: Draw posterior-predictive flows for the OLS gravity model.

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

Return the PyMC model object built for the most recent fit.

For Gibbs-fitted models the PyMC model is not constructed during sampling; it is built lazily on first access so that downstream consumers (e.g. bridge sampling for marginal likelihoods) can evaluate logp and the prior under the same model definition used by the NUTS path.

Returns:¶: The model object used by fit(), or None if the instance has not been fit yet.
Return type:¶: pymc.Model or None

residuals()[source]¶

Return residuals y - fitted_values.

Returns:¶: Residual vector y - fitted_values on the same scale as fitted_values().
Return type:¶: np.ndarray

spatial_diagnostics()[source]¶

Run Bayesian LM specification tests and return a summary table.

Looks up the diagnostic suite registered for this model class and calls each test function on this fitted model, collecting the results into a tidy DataFrame. The set of tests depends on the model type — for example, an OLS model runs LM-Lag, LM-Error, LM-SDM-Joint, and LM-SLX-Error-Joint, while an SAR model runs LM-Error, LM-WX, and Robust-LM-WX. Panel models run the Panel--prefixed analogues (e.g. Panel-LM-Lag).

Requires the model to have been fit (.fit() called) and a spatial weights matrix W to have been supplied at construction time.

Returns:¶

DataFrame indexed by test name with columns:

Column	Description
statistic	Posterior mean of the LM statistic
median	Posterior median of the LM statistic
df	Degrees of freedom for the \(\chi^2\) reference
p_value	Bayesian p-value: `1 - chi2.cdf(mean, df)`
ci_lower	Lower bound of 95% credible interval (2.5%)
ci_upper	Upper bound of 95% credible interval (97.5%)

The DataFrame has attrs["model_type"] (class name) and attrs["n_draws"] (total posterior draws) metadata.

Return type:¶

pandas.DataFrame

Raises:¶

RuntimeError – If the model has not been fit yet.
ValueError – If no spatial weights matrix W was supplied.

See also

spatial_diagnostics_decision: Model-selection decision based on the test results.
spatial_effects: Posterior inference for direct/indirect/total impacts.

Examples

>>> ols = OLS(formula="price ~ income + crime", data=df, W=w)
>>> ols.fit()
>>> ols.spatial_diagnostics()
                 statistic  median  df  p_value  ci_lower  ci_upper
LM-Lag                3.21    2.98   1    0.073      0.12      8.54
LM-Error              5.67    5.34   1    0.017      0.34     12.10
LM-SDM-Joint          7.89    7.12   4    0.096      1.23     18.32
LM-SLX-Error-Joint    6.45    5.98   4    0.168      0.89     15.67

spatial_diagnostics_decision(alpha=0.05, format='graphviz')[source]¶

Return a model-selection decision from Bayesian LM test results.

Walks the flow decision tree using Bayesian p-values from spatial_diagnostics() and recommends either the OLS flow baseline (no spatial dependence detected) or the SAR flow model (at least one direction is significant).

Parameters:¶

alpha : float, default 0.05¶: Significance level for the Bayesian p-values.
format : {"graphviz", "ascii", "model"}, default "graphviz"¶: Output format. "model" returns the recommended model name string. "ascii" returns an indented box-drawing tree. "graphviz" returns a graphviz.Digraph (with ASCII fallback if graphviz is not installed).

Return type:¶

str or graphviz.Digraph

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

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

Wraps _compute_spatial_effects_posterior() to produce a tidy DataFrame indexed by predictor with posterior means, credible-interval bounds, and Bayesian p-values for each effect type (origin, destination, intra, network, total). Following Thomas-Agnan & LeSage (2014, §83.5.2), when destination and origin design blocks differ the decomposition is reported separately for shocks applied to each side.

Parameters:¶

draws : int, optional¶: Maximum number of posterior draws to use. Defaults to all.
return_posterior_samples : bool, default False¶: If True, also return the underlying posterior-draw arrays.
ci : float, default 0.95¶: Credible-interval coverage.
mode : {"auto", "combined", "separate"}, default "auto"¶: Controls whether destination- and origin-side effects are summed or reported separately. "auto" collapses to combined when the destination and origin design blocks are identical (self._symmetric_xo_xd) and reports both sides otherwise. "combined" always sums; "separate" always reports both.

Returns:¶

Long-format summary indexed by (predictor, side, effect) where side is one of "combined", "dest", "orig".

Return type:¶

pandas.DataFrame, or (DataFrame, dict)

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

Return posterior summary table.

Parameters:¶

var_names : list, optional¶: Variable names to include in the summary.
**kwargs¶: Additional arguments passed to arviz.summary().

Returns:¶

Posterior summary statistics.

Return type:¶

pandas.DataFrame