statsmodels.miscmodels.count.PoissonZiGMLE

class statsmodels.miscmodels.count.PoissonZiGMLE(endog, exog=None, offset=None, missing='none', **kwds) [source]

Maximum Likelihood Estimation of Poisson Model

This is an example for generic MLE which has the same statistical model as discretemod.Poisson but adds offset and zero-inflation.

Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are based on numerical differentiation.

There are numerical problems if there is no zero-inflation.

Methods

expandparams(params)	expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, …])	Fit the model using maximum likelihood.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
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.
loglikeobs(params)
nloglike(params)
nloglikeobs(params)	Loglikelihood of Poisson model
predict(params[, exog])	After a model has been fit predict returns the fitted values.
reduceparams(params)
score(params)	Gradient of log-likelihood evaluated at params
score_obs(params, **kwds)	Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables