statsmodels.distributions.discrete.DiscretizedModel¶

class statsmodels.distributions.discrete.DiscretizedModel(endog, exog=None, distr=None)[source]¶

experimental model to fit discretized distribution

Count models based on discretized distributions can be used to model data that is under- or over-dispersed relative to Poisson or that has heavier tails.

Parameters:¶

endogarray_like, 1-D

Univariate data for fitting the distribution.

exogNone

Explanatory variables are not supported. The exog argument is only included for consistency in the signature across models.

distrDiscretizedCount instance

(required) Instance of a DiscretizedCount distribution.

See also

DiscretizedCount

Examples

>>> from scipy import stats
>>> from statsmodels.distributions.discrete import (
        DiscretizedCount, DiscretizedModel)

>>> dd = DiscretizedCount(stats.gamma)
>>> mod = DiscretizedModel(y, distr=dd)
>>> res = mod.fit()
>>> probs = res.predict(which="probs", k_max=5)

Attributes:¶

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

Methods

expandparams(params)	expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...])	Fit method for likelihood based models
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
get_distr(params)	frozen distribution instance of the discrete distribution.
hessian(params)	Hessian of log-likelihood evaluated at params
hessian_factor(params[, scale, observed])	Weights for calculating Hessian
information(params)	Fisher information matrix of model.
initialize()	Initialize (possibly re-initialize) a Model instance.
loglike(params)	Log-likelihood of model at params
loglikeobs(params)	Log-likelihood of the model for all observations at params.
nloglike(params)	Negative log-likelihood of model at params
predict(params[, exog, which, k_max])	After a model has been fit predict returns the fitted values.
reduceparams(params)	Reduce parameters
score(params)	Gradient of log-likelihood evaluated at params
score_obs(params, **kwds)	Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.