class statsmodels.miscmodels.count.PoissonZiGMLE(endog, exog=
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.
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.
Hessian of log-likelihood evaluated at params
hessian_factor(params[, scale, observed])
Weights for calculating Hessian
Fisher information matrix of model.
Initialize (possibly re-initialize) a Model instance.
Log-likelihood of model at params
Log-likelihood of the model for all observations at params.
Negative log-likelihood of model at params
Loglikelihood of Poisson model
After a model has been fit predict returns the fitted values.
Gradient of log-likelihood evaluated at params
Jacobian/Gradient of log-likelihood evaluated at params for each observation.
Names of endogenous variables.
Names of exogenous variables.