bayespecon.models.SpatialProbit¶

class bayespecon.models.SpatialProbit(formula=None, data=None, y=None, X=None, W=None, region_col=None, region_ids=None, mobs=None, priors=None, robust=False)[source]¶

Bayesian spatial probit with regional random effects.

Parameters:¶

formula : str, optional¶: Formula for the binary response model, e.g. "y ~ x1 + x2". Requires data and region_col.
data : pandas.DataFrame, optional¶: Data source used with formula mode.
y : array-like, optional¶: Binary dependent variable (0/1), required in matrix mode.
X : array-like or pandas.DataFrame, optional¶: Covariate matrix, required in matrix mode.
W : libpysal.graph.Graph or scipy.sparse matrix¶: Region-level m x m spatial weights matrix. Accepts a libpysal.graph.Graph (the modern libpysal graph API) or any scipy.sparse matrix. The legacy libpysal.weights.W object is not accepted directly; pass w.sparse or convert with libpysal.graph.Graph.from_W(w). W should be row-standardised; a UserWarning is raised if not.
region_col : str, optional¶: Region identifier column in data (formula mode).
region_ids : array-like, optional¶: Region identifier per observation (matrix mode).
mobs : array-like, optional¶: Region observation counts (m,) in sorted region order (matrix mode alternative to region_ids).
priors : dict, optional¶: Prior overrides.

Notes

This class follows the core semip_g structure (binary response with spatially dependent regional effects). It uses a standard probit link with unit observation-level variance and does not currently sample the v_i/ r heteroskedastic hierarchy from MATLAB semip_g.

Priors (priors keys):

rho_lower, rho_upper: bounds for rho (default -0.95, 0.95)
beta_mu, beta_sigma: Normal prior for beta
sigma_a_sigma: HalfNormal scale for sigma_a

Robust regression

robust=True is not supported for SpatialProbit. The probit link function uses a Normal CDF; a robust version would require a Student-t CDF link, which is not yet implemented. Use robust=True with Gaussian models (OLS, SAR, SEM, etc.) instead.

__init__(formula=None, data=None, y=None, X=None, W=None, region_col=None, region_ids=None, mobs=None, priors=None, robust=False)[source]¶

Methods

`__init__`([formula, data, y, X, W, ...])
`fit`([draws, tune, chains, target_accept, ...])	Draw samples from the posterior.
`fitted_probabilities`()	Return posterior mean fitted probabilities for observed data.
`random_effects_mean`()	Return posterior mean regional effects.
`summary`([var_names])	Return posterior summary table.

Attributes

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

fit(draws=2000, tune=1000, chains=4, target_accept=0.9, random_seed=None, **sample_kwargs)[source]¶: Draw samples from the posterior.

fitted_probabilities()[source]¶: Return posterior mean fitted probabilities for observed data.

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

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

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

random_effects_mean()[source]¶: Return posterior mean regional effects.

summary(var_names=None, **kwargs)[source]¶: Return posterior summary table.