statsmodels.miscmodels.count.PoissonZiGMLE

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

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.

Parameters:

endogarray_like

1-d endogenous response variable. The dependent variable.

exogarray_like, optional

A nobs x k array where nobs is the number of observations and k is the number of regressors. If None, a column of ones is used.

offsetarray_like, optional

Offset added to the linear predictor before computing the mean.

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

**kwds

Extra keyword arguments passed to the model.

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 the model via 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 at params
loglikeobs(params)	Log-likelihood of the model for all observations at params
nloglike(params)	Negative log-likelihood of model at params
nloglikeobs(params)	Loglikelihood of Poisson model
predict(params[, exog])	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

Methods

expandparams(params)	Expand to full parameter array when some parameters are fixed
fit([start_params, method, maxiter, ...])	Fit the model via 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 at params
loglikeobs(params)	Log-likelihood of the model for all observations at params
nloglike(params)	Negative log-likelihood of model at params
nloglikeobs(params)	Loglikelihood of Poisson model
predict(params[, exog])	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